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Are you taking a hard look at your cloud costs this budgeting season? Is your IT budget getting eaten up without a good ROI to show for it? Do you need to justify your cloud expenses and business cases to leadership?

At Data Summit 2024, Michael Agarwal, director and global practice leader, site reliability engineering, cloud and NoSQL databases, Datavail, discussed cloud cost optimization strategies, tips, and best practices during his session, “Cloud Cost Optimization: Strategies, Tips, & Best Practices.”

The annual Data Summit conference returned to Boston, May 8-9, 2024, with pre-conference workshops on May 7.

He discussed right-sizing cloud resources, monitoring, and managing your cloud usage effectively, identifying and eliminating wasteful spending, leveraging automation for better cost control, exploring cloud pricing models and modernization opportunities, and achieving the right balance of price and performance for your business requirements.

“Why do we want to optimize costs?” Agarwal said. “It’s going to help your company save money. If everyone in the world does cost optimization, you’re also helping our planet.”

The AWS cost infrastructure runs at an hourly usage rate, you pay for what you use and for what features you need, he explained.

“Before you can optimize, you have to understand how they are charging us,” Agarwal said.

If you’re using AWS, he recommends staying on top of your usage by enabling multiple billing alerts, analyzing trends and costs on a regular basis.

“You can do process automation on things that you don’t need,” he said. “All these companies have tools to help you understand [your costs].”

AWS also provides:

• AWS Trusted Advisor

• Cost optimizer for Amazon Workspaces

• Instance scheduler on AWS

• AWS Cost Anomaly Detection
• AWS Compute Optimizer

The EC2/RDS pricing model offers reserved instances/savings plans that can provide up to 72% cost savings compared to on-demand instances.

Lifecycle policies on storage can help companies to purge what isn’t needed, he said. You can set the S3 bucket lifecycle policies for auto purge and archive. Then you can choose different S3 storage types such as reduced redundancy, infrequent access, standard, or Glacier.

“The difference is the speed,” Agarwal said. “Picking the right type will help you with your costs.”

Many Data Summit 2024 presentations are available for review at https://www.dbta.com/DataSummit/2024/Presentations.aspx.

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MongoDB (MDB) came out with a quarterly loss of $0.09 per share versus the Zacks Consensus Estimate of a loss of $0.23. This compares to loss of $0.33 per share a year ago. These figures are adjusted for non-recurring items.

This quarterly report represents an earnings surprise of 60.87%. A quarter ago, it was expected that this database platform would post a loss of $0.41 per share when it actually produced a loss of $0.11, delivering a surprise of 73.17%.

Over the last four quarters, the company has surpassed consensus EPS estimates four times.

MongoDB , which belongs to the Zacks Internet – Software industry, posted revenues of $266.49 million for the quarter ended January 2022, surpassing the Zacks Consensus Estimate by 10.48%. This compares to year-ago revenues of $171 million. The company has topped consensus revenue estimates four times over the last four quarters.

The sustainability of the stock’s immediate price movement based on the recently-released numbers and future earnings expectations will mostly depend on management’s commentary on the earnings call.

MongoDB shares have lost about 47.1% since the beginning of the year versus the S&P 500’s decline of -11.9%.

What’s Next for MongoDB?

While MongoDB has underperformed the market so far this year, the question that comes to investors’ minds is: what’s next for the stock?

There are no easy answers to this key question, but one reliable measure that can help investors address this is the company’s earnings outlook. Not only does this include current consensus earnings expectations for the coming quarter(s), but also how these expectations have changed lately.

Empirical research shows a strong correlation between near-term stock movements and trends in earnings estimate revisions. Investors can track such revisions by themselves or rely on a tried-and-tested rating tool like the Zacks Rank, which has an impressive track record of harnessing the power of earnings estimate revisions.

Ahead of this earnings release, the estimate revisions trend for MongoDB: mixed. While the magnitude and direction of estimate revisions could change following the company’s just-released earnings report, the current status translates into a Zacks Rank #3 (Hold) for the stock. So, the shares are expected to perform in line with the market in the near future. You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.

It will be interesting to see how estimates for the coming quarters and current fiscal year change in the days ahead. The current consensus EPS estimate is -$0.28 on $250.06 million in revenues for the coming quarter and -$0.95 on $1.13 billion in revenues for the current fiscal year.

Investors should be mindful of the fact that the outlook for the industry can have a material impact on the performance of the stock as well. In terms of the Zacks Industry Rank, Internet – Software is currently in the bottom 32% of the 250 plus Zacks industries. Our research shows that the top 50% of the Zacks-ranked industries outperform the bottom 50% by a factor of more than 2 to 1.

One other stock from the same industry, Asana, Inc. (ASAN), is yet to report results for the quarter ended January 2022. The results are expected to be released on March 9.

This company is expected to post quarterly loss of $0.28 per share in its upcoming report, which represents a year-over-year change of -27.3%. The consensus EPS estimate for the quarter has remained unchanged over the last 30 days.

Asana, Inc.’s revenues are expected to be $105.07 million, up 53.7% from the year-ago quarter.

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In a wide-ranging interview with CRN at last week’s MongoDB.local NYC event, CEO Dev Ittycheria offered his views on why AI development is about to enter a more “transformative” phase, the thinking behind the company’s MongoDB AI Application Program to accelerate AI application development, and the role partners are playing in the AI wave.

MongoDB occupies a critical position in the AI wave now sweeping the IT industry. With MongoDB Atlas, the company’s next-generation cloud database and development platform, along with related products, its strategic alliances with all three cloud hyperscalers, and its extensive base of channel and technology partners, the New York-based company is well-positioned for what comes next in AI.

Last week at the company’s MongoDB.local NYC event, president and CEO Dev Ittycheria, in a keynote speech (pictured), said AI is at a “crucible moment” – defined as the inflection points in time when pivotal decisions are made that establish long-term trends and determine a technology’s trajectory.

“We’re at a time where every company is facing a crucible moment when it comes to AI. AI can completely reimagine how you run your business, how you engage with your customers, and how you become more nimble and efficient,” Ittychria said.

[Related: The Coolest Database System Companies Of The 2024 Big Data 100]

Ittycheria compared the current AI wave to previous tectonic shifts in the IT industry including the internet, the iPhone, and cloud computing. Each was marked by early (and sometimes failed) businesses and tech products followed by more transformative technologies. (The CEO even showed a graphic of a newspaper headline from 2000 asking whether the Internet was “just a passing fad.”)

Ittycheria said the real value of AI will be generated by the applications and services that are developed and run on platforms like MongoDB Atlas. “It’s a no-brainer that you’re going to see an explosion in the number of [AI-based] applications built this coming year.”

To that end, at the event, MongoDB launched its MAAP (MongoDB AI Application Program) initiative that provides a complete technology stack, services and other resources to help businesses develop and deploy at scale applications with advanced generative AI capabilities. MAAP, with MongoDB Atlas at its core, includes reference architectures and technology from a who’s who in the AI space including all three cloud platform giants, LLM (large language model) tech developers Cohere and Anthropic, and a number of AI development tool companies including Fireworks.ai, LlamaIndex and Credal.ai.

In addition, MongoDB unveiled a number of new MongoDB Atlas capabilities that make it easier for businesses and organizations to build, deploy and run modern applications – including those with generative AI capabilities.

Those unveilings included the general availability of MongoDB Atlas Stream Processing for developing applications that work with streaming “data in motion,” the public preview of MongoDB Atlas Edge Server for deploying and operating distributed applications in the cloud and at the edge, and the general availability of MongoDB Atlas Search Nodes on AWS and Google Cloud and in preview mode for Microsoft Azure.

MongoDB, meanwhile, has enjoyed robust growth. For its fiscal 2024 (ended Jan. 31, 2024) MongoDB reported total revenue of $1.68 billion, up 27 percent year over year, highlighted by 34-percent growth in MongoDB Atlas revenue.

In a wide-ranging interview with CRN at the MongoDB.local NYC event, Ittycheria offered his views on why AI development is about to enter a more “transformative” phase, the thinking behind the company’s MongoDB AI Application Program to accelerate AI application development, and the role partners are playing in the AI wave. His answers have been lightly edited for clarity and length.

In your keynote speech you said that AI is at a “crucible moment” right now. How so?

I think it’s a pending crucible moment, how people essentially prepare and leverage [AI] for the future. The point I was trying to say is it hasn’t completely transformed my life – not yet. The analogy I’d use is like 1998 and 2000. The internet had not completely transformed my life just yet, but it was soon going to do so. I started to help businesses leveraging the internet, my personal life was affected by the internet. Even today, if internet access goes out, all hell breaks loose.

So why do you think the crucible moment is still pending? We’re not at an AI crucible moment just yet?

I think, to me, it’s back to [the fact] there’s lots of investments being made at the infrastructure layer of AI, building out compute, training models, putting in all the foundational elements for AI. But the first generation of AI apps that we’re seeing, these chatbot tools that do research, summarize information and generate content. They serve some utility, but they are not truly transformative.

One point I was making was that also happened when the first set of apps for the iPhone came out. They were nice utilities. But then when you saw how those apps ultimately affected every [person] in their lives and how they lived, how they worked, how they collaborated, that was delivered through the apps. And so the point I was making is that I think today we just have some interesting, nice [AI] utilities to serve some function or purpose. But they’re not transformative, as they will be in the future.

As you said in your keynote, as happened with the internet in early days, is there a danger of overstating or overestimating the impact of AI, either right now or in the future?

Every morning I wake up to something new in AI. Some new company gets funding, some new company is rolling out a new service or capability, or [there is] some new worry about safety, the negative implications of AI – there’s always something coming out. There’s a lot of interest around AI. But I do think, like with any tech adoption that has happened to us, it’s very easy to over-extrapolate in the short term, but underestimate in the long term.

I think the point of my keynote was to show that what happened in 2000, some people considered the internet a fad because in March of 2000 the bubble burst and they said, ‘This is just a lot of hype.’ And it was at that exact moment that the seeds were being planted for a new generation of apps and services that transformed my life.

So the first wave of AI applications that you just mentioned, what would be the easy ones? What would be an example of that?

We see a lot of customers rolling out chat bots for support services. It’s a relatively easy app because you have your own corporate data. We’re rolling out support agents. We have the corporate data, best practices, common errors that customers make, common misconfigurations, common questions. And it’s quite beneficial because instead of, you know, requiring a human to respond, you can very quickly get some bots to respond back with accurate information and that helps both the customer and helps us. It helps the customer get the answer quickly and helps us because we don’t have to expend a lot of resources and cost to do so.

So those are what I call almost no-brainer kind of apps that people are using. And you see some people using that for internal use, like we have an internal chatbot for our sales and marketing teams if they want to find information about a product and want to find information about how to handle, potentially, a question that a customer has or how to potentially put a proposal together. We have all these things, there’s all this institutional knowledge in our platform company that we can quickly bring to the fingertips of our people. I’m not saying it’s not of value, but it’s not exactly transformative. It’s just a way to become more efficient.

So what is your vision of how AI applications will evolve? What will the next wave of applications look like?

I’m starting to see signs – I’ll give you a good example. We have an automotive company [customer] who, obviously, as you can imagine, get calls from their customers when something’s not working, their car’s not working properly. What they have done is, they have recorded what the engine sounds like and to correlate engine sounds to the root-cause issue.

Now what they can do [using AI applications] is, when you’re driving one of their cars and you’re having a problem, they’ll ask if you can record the sound of the engine of your car and send that sound file to them. And that file will be used to diagnose – with a high probability of [accuracy] – what the problem is. So if you’re thinking that your car may have a funny noise, it could be, using simple analysis, that your brakes need to be replaced because they squeak or your carburetor needs to be repaired or replaced.

So that’s an interesting way, a whole new modality, to essentially troubleshoot. I don’t require the customer to come to my shop, leave the car for a couple days. Now they say, ‘We know what the problem is. We’re going to order the part for you. Show up tomorrow, the day after, and you’ll be in and out very, very quickly.’ That’s an example of something that’s a little bit more transformative than a chatbot.

There’s no question customers are feeling overwhelmed. And I would say a combination of overwhelmed and fearful. They’re overwhelmed because the rate and pace in innovation in AI is so high, it’s almost like there’s a new announcement every week. Meta came out with Meta Llama 3 [April 18] – their benchmarks apparently are as good as OpenAI. ChatGPT: some people are saying maybe I shouldn’t bet on open source. A couple weeks earlier Mistral

came out with a very low-cost model that was as good as ChatGPT 4. Before that Anthropic came out with a new model. So the rate and pace of change in AI is very, very high. People are feeling overwhelmed, which means they get paralyzed.

But on the other side of the coin, they’re fearful. They’re fearful because if I don’t do anything, don’t act and try to take advantage of new technology, there are risks that my competition will do so and potentially not just marginalize me but potentially disrupt me. That’s the tension that customers are feeling.

And that’s the whole purpose behind our MAAP program. With the MongoDB Application Program they come out with a validated set of reference architectures for a bunch of use cases, built-in integrations that can get started right away, and technical expertise to get you up and running as fast as possible.

But to your point, we’re not locking people into any one ecosystem. We work with any cloud, any LLM, any orchestration tool, any fine-tuning model. The point is, the only way to overcome this is you’ve got to start somewhere. You can’t just sit on the sidelines [and] do nothing. But you also don’t want to put all your eggs in one basket. So our point is, get started, try some projects, learn. And through that experience you’ll find out what’s important to you and what’s most impactful for you. And you’ll gain confidence – or maybe lose confidence, based on the technologies you’re using – and decide, ‘Okay, I want to change this component, maybe this orchestration layer I was using just doesn’t fit me, I’m going to try something else.’ But it allows them to learn without taking an exorbitant amount of risk.

Talk a little bit more about how the MAAP initiative addresses these issues. How will providing all these components in one environment help people get past the fear and the feeling of being overwhelmed.

Well, one thing is, people need databases. So it’s not like your database technology is going to change. You can feel pretty comfortable betting on MongoDB. If they feel like, “Hey, I’ve started with OpenAI, but I’d rather use [Meta AI] Llama.” It’s very easy with APIs to all the large language models. And so that becomes relatively easy to point your API from one foundational model to another if you’re using a certain set of orchestration layers, tools like llamaindex or LangChain. It’s not that difficult. We have integrations with all those tools so that you can decide which one fits the way you want to work.

And then there’s fine tuning tools, and so on and so forth. You can still use Fireworks for AI integration and model hosting capabilities. So depending on what your model is – some customers want to do everything in house, they want to use open source and they may run the models in house. Some customers want to start in the cloud. You can start with Amazon, you can start with Azure. But if you find [that] most of your data is in AWS, maybe you stay on AWS, we give you that flexibility to do so. We’re the only one that partners with all three [cloud hyperscalers].

We’ve written quite a bit about MongoDB’s strategic relationships with the three hyperscalers: AWS, Microsoft Azure and Google Cloud, and how MongoDB works with them even though some of them have competing products. What’s their role here?

We’re very popular in all three clouds. There’s tons of MongoDB usage in all three clouds. [The hyperscalers] are doing it to ensure that their own customers are using MongoDB to get access to all the Mongo DB capabilities. And it’s not just from an integration point of view. It’s from the go-to-market – we’re in all the co-sell programs they have, we’re on the first-party consoles. We’re the only ISV featured in all their startup programs. So startups, whether they’re building on AWS, Google [Cloud] or Azure, can leverage MongoDB. Those are examples of the fact that we really try and make it easy for our customers. No matter where they are, they have the freedom to run their workloads anywhere.

What kind of opportunities does the MAAP initiative create for MongoDB’s partner base beyond the specific launch partners, including systems integrators (SIs) and ISVs?

Boutique SIs are part of the program. And the reason we started there is because we want to start with partners who really know MongoDB and really know some of the integrations we’ve built. In terms of people, Accenture and other large SIs have lots of resources, but not all of them are trained on MongoDB. You can expect us to expand to the larger SIs. But we wanted to start out of the gate with people who could really hit the ground running.

I do expect the partnerships to expand over time [and] you will see us adding more partners to the program. There’ll be regional partners by geo[ography], there’ll be industry-specific partners by vertical industry. And there’ll be domain-specific partners with particular use cases or technology.

The key takeaway I would want to leave with you is that partnerships are a core element of this. We know we can’t do it all on our own.

We wanted to come out of the gate – what customers really appreciate is a perspective and a point of view. What they don’t want is, ‘I can do anything you want. Tell me what you want.’ That’s not very helpful. ‘Give me a starting point, give me a place where I can start and get going.’ And as they get more experience and all that they’ll start outperforming their own point of view. We’re trying to help them.

And when we talk to customers, we’re not cloud dependent or partial to any one cloud. We’re not partial to any one LLM. And so customers feel like, ‘Okay, I can trust your feedback because it’s not like you have one set of tricks you’re trying to sell me.’ My point is, it’s not like we have a finite set of solutions and every answer is our own solution. That gives us more credibility with our customers.

In addition to MAAP, there were a series of announcements at the MongoDB event including the general availability of MongoDB Atlas Stream Processing, the general availability of MongoDB Atlas Vector Search integrated with Amazon Bedrock, and collaboration with Google Cloud to optimize Gemini Code Assist for building applications on MongoDB. How do partners benefit from these expanded capabilities and what kind of opportunities do they create for partners?

So what partners can do – because we have a lot of people who are building applications on top of MongoDB – is that now they can have a much more simplified architecture to do things like event-based applications with stream processing. It’s all designed around dealing with live data and data in motion. That’s happening. And you have to react to those events and figure out how you respond to events. And so we make it easy for partners to build these event-driven applications, which to me is the modern app of the future. You can’t have a static app that can’t deal with new data. For our partners, it just enables them to have a much more simplified architecture because the stream processing and the OLTP engine of MongoDB is all essentially one platform, so it’s much easier and much more liberating to build on MongoDB than to have to use a bunch of bespoke technologies.

The same with Edge Server. We’ve talked about how to run in the cloud, all the processes on the cloud. But as you know, if you have customers now, like a retail customer who has stores in a particular geography, a country or region, they need to do local processing. Bringing intelligence down to the edge is an important element, you can’t run everything in the cloud. Over time you’re going to start even running models on your devices – Apple is already making some noises about how that’s going to happen soon. So you need to be able to process and deal with models that could be in devices. Or it could be on edge servers, say, in a store, it could be in a hospital where you need very quick access. There’s a whole host of new use cases that are going to emerge.

In your keynote you mentioned MongoDB’s investments in a half-dozen “boutique” systems integrators including PeerIslands, Pureinsights and Gravity 9. They are obviously a key part of MAAP. Do you foresee additional investments like this?

One of our board members is a gentleman named Frank D’Souza who was co-founder and CEO of Cognizant [Technology Solutions]. And as you know, Cognizant became a very, very large systems integrator. What he shared with us – it was his advice that we ultimately acted on – was that the challenge the largest SIs have is that they’re so large [and] they’re also so distributed, [that] just because you get one team to understand and apply MongoDB for one or two accounts does not mean the rest of the organization has the capability or the wherewithal to do so.

So his point was, invest in SIs who become experts and know how to build applications on top of MongoDB. And then ultimately, once they get to a reasonable size scale, you know, someone like an Accenture or an Infosys or TCS may come in and acquire that company. Accenture buys a new company, like, almost every week. That’s the way that they inculcate new technologies into their organizations because it’s too hard to do so organically.

And so that’s part of our strategy if we want to inculcate a bunch of systems integrators who have become deep experts in MongoDB, whether it’s just building new applications on MongoDB or migrating legacy applications onto MongoDB. And we see huge demand for that.

And by the way, we’ve already done a lot of business with Accenture and TCS and Infosys and Cognizant and Capgemini and all that. In fact, I have a call with one of the top leaders of Accenture tomorrow. We don’t want to be in the systems integration business. This is more of a way to help build up a competency that one of the large SIs may find very attractive. And there’s analogues: Salesforce does this themselves. They had a tough time getting large SIs to focus on Salesforce technology. As their business grew, they invested in these boutique SIs that ultimately were acquired by the larger SIs.

I know MongoDB has been quite bullish on its financials given its 27-percent revenue growth in fiscal 2024. How is this year looking? Are you seeing any impact of the uncertain economy? And is this AI wave immune to the economy?

I can’t comment too much on the [fiscal 2025 first quarter] quarter because that would be non-public information. A year ago we started seeing signs of the economy slowing down – that’s when the Fed [U.S. Federal Reserve] was raising rates [and] interest rates were spiking. And what we said was, you know, organizations are kind of in a conundrum, they have to figure out how to do more with less, but they also have to invest in AI to really figure out how they can leverage this new technology for a competitive advantage.

And so I think customers are very interested in becoming more confident on AI and figuring out how, you know – the questions they ask are where to get started. ‘What use-cases should I focus on and what tech stack I should use?’ That being said, they’re being very prudent because there’s got to be a compelling return on investment for them.

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Victory Capital Management Inc. raised its holdings in MongoDB, Inc. (NASDAQ:MDB – Free Report) by 43.9% in the 4th quarter, according to its most recent 13F filing with the Securities & Exchange Commission. The firm owned 55,818 shares of the company’s stock after buying an additional 17,042 shares during the period. Victory Capital Management Inc. owned approximately 0.08% of MongoDB worth $22,821,000 as of its most recent SEC filing.

Several other hedge funds and other institutional investors also recently added to or reduced their stakes in the business. Raymond James & Associates raised its stake in MongoDB by 14.2% during the 4th quarter. Raymond James & Associates now owns 60,557 shares of the company’s stock worth $24,759,000 after acquiring an additional 7,510 shares in the last quarter. Nordea Investment Management AB increased its stake in MongoDB by 298.2% in the fourth quarter. Nordea Investment Management AB now owns 18,657 shares of the company’s stock valued at $7,735,000 after purchasing an additional 13,972 shares during the period. Tokio Marine Asset Management Co. Ltd. increased its stake in MongoDB by 9.7% in the third quarter. Tokio Marine Asset Management Co. Ltd. now owns 1,903 shares of the company’s stock valued at $658,000 after purchasing an additional 168 shares during the period. Assenagon Asset Management S.A. increased its stake in MongoDB by 1,196.1% in the fourth quarter. Assenagon Asset Management S.A. now owns 29,215 shares of the company’s stock valued at $11,945,000 after purchasing an additional 26,961 shares during the period. Finally, Realta Investment Advisors acquired a new position in shares of MongoDB during the fourth quarter worth about $212,000. Hedge funds and other institutional investors own 89.29% of the company’s stock.

Insider Transactions at MongoDB

In other news, CEO Dev Ittycheria sold 17,160 shares of the stock in a transaction on Tuesday, April 2nd. The shares were sold at an average price of $348.11, for a total value of $5,973,567.60. Following the completion of the sale, the chief executive officer now owns 226,073 shares of the company’s stock, valued at $78,698,272.03. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which is available through this hyperlink. In other news, CEO Dev Ittycheria sold 17,160 shares of the company’s stock in a transaction on Tuesday, April 2nd. The shares were sold at an average price of $348.11, for a total value of $5,973,567.60. Following the completion of the transaction, the chief executive officer now directly owns 226,073 shares in the company, valued at $78,698,272.03. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available through the SEC website. Also, CRO Cedric Pech sold 1,430 shares of the company’s stock in a transaction on Tuesday, April 2nd. The shares were sold at an average price of $348.11, for a total transaction of $497,797.30. Following the completion of the transaction, the executive now owns 45,444 shares of the company’s stock, valued at $15,819,510.84. The disclosure for this sale can be found here. Insiders sold 46,802 shares of company stock valued at $16,514,071 over the last 90 days. Corporate insiders own 4.80% of the company’s stock.

MongoDB Trading Up 0.7 %

MDB stock traded up $2.57 on Thursday, hitting $355.77. 282,803 shares of the company were exchanged, compared to its average volume of 1,344,229. MongoDB, Inc. has a twelve month low of $255.06 and a twelve month high of $509.62. The company has a debt-to-equity ratio of 1.07, a current ratio of 4.40 and a quick ratio of 4.40. The business’s fifty day moving average is $366.16 and its two-hundred day moving average is $391.13. The stock has a market capitalization of $25.91 billion, a price-to-earnings ratio of -142.42 and a beta of 1.19.

MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings results on Thursday, March 7th. The company reported ($1.03) EPS for the quarter, missing the consensus estimate of ($0.71) by ($0.32). MongoDB had a negative return on equity of 16.22% and a negative net margin of 10.49%. The company had revenue of $458.00 million for the quarter, compared to analyst estimates of $431.99 million. As a group, research analysts forecast that MongoDB, Inc. will post -2.53 earnings per share for the current year.

Analysts Set New Price Targets

A number of analysts recently commented on the stock. Truist Financial boosted their price objective on shares of MongoDB from $440.00 to $500.00 and gave the stock a “buy” rating in a report on Tuesday, February 20th. Redburn Atlantic reissued a “sell” rating and set a $295.00 price objective (down previously from $410.00) on shares of MongoDB in a report on Tuesday, March 19th. Loop Capital assumed coverage on shares of MongoDB in a report on Tuesday, April 23rd. They set a “buy” rating and a $415.00 price objective on the stock. Guggenheim boosted their price objective on shares of MongoDB from $250.00 to $272.00 and gave the stock a “sell” rating in a report on Monday, March 4th. Finally, KeyCorp decreased their price objective on shares of MongoDB from $490.00 to $440.00 and set an “overweight” rating on the stock in a report on Thursday, April 18th. Two research analysts have rated the stock with a sell rating, three have given a hold rating and twenty have given a buy rating to the company. According to data from MarketBeat.com, MongoDB currently has a consensus rating of “Moderate Buy” and a consensus price target of $443.86.

Check Out Our Latest Report on MongoDB

About MongoDB

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MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Article originally posted on mongodb google news. Visit mongodb google news

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M&T Bank Corp raised its stake in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 7.2% during the 4th quarter, HoldingsChannel reports. The firm owned 6,309 shares of the company’s stock after purchasing an additional 424 shares during the quarter. M&T Bank Corp’s holdings in MongoDB were worth $2,580,000 as of its most recent filing with the Securities & Exchange Commission.

A number of other institutional investors also recently modified their holdings of the company. Raymond James & Associates lifted its position in shares of MongoDB by 34.8% during the 3rd quarter. Raymond James & Associates now owns 53,047 shares of the company’s stock worth $18,347,000 after purchasing an additional 13,686 shares during the last quarter. Zurcher Kantonalbank Zurich Cantonalbank lifted its holdings in shares of MongoDB by 3.8% during the third quarter. Zurcher Kantonalbank Zurich Cantonalbank now owns 30,014 shares of the company’s stock worth $10,381,000 after buying an additional 1,087 shares during the last quarter. Mackenzie Financial Corp boosted its position in shares of MongoDB by 19.9% in the third quarter. Mackenzie Financial Corp now owns 4,966 shares of the company’s stock valued at $1,647,000 after acquiring an additional 824 shares during the period. Strs Ohio acquired a new stake in shares of MongoDB in the third quarter worth $626,000. Finally, Mirae Asset Global Investments Co. Ltd. increased its position in MongoDB by 30.5% during the third quarter. Mirae Asset Global Investments Co. Ltd. now owns 21,283 shares of the company’s stock worth $7,173,000 after acquiring an additional 4,971 shares during the period. Institutional investors own 89.29% of the company’s stock.

MongoDB Trading Down 0.6 %

MDB stock opened at $353.20 on Thursday. The business has a fifty day moving average price of $366.16 and a 200 day moving average price of $391.13. The company has a debt-to-equity ratio of 1.07, a current ratio of 4.40 and a quick ratio of 4.40. MongoDB, Inc. has a fifty-two week low of $244.85 and a fifty-two week high of $509.62. The stock has a market capitalization of $25.72 billion, a price-to-earnings ratio of -142.42 and a beta of 1.19.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its earnings results on Thursday, March 7th. The company reported ($1.03) earnings per share for the quarter, missing analysts’ consensus estimates of ($0.71) by ($0.32). MongoDB had a negative net margin of 10.49% and a negative return on equity of 16.22%. The business had revenue of $458.00 million during the quarter, compared to analyst estimates of $431.99 million. Analysts expect that MongoDB, Inc. will post -2.53 EPS for the current fiscal year.

Wall Street Analysts Forecast Growth

A number of analysts have weighed in on the stock. Tigress Financial increased their price target on shares of MongoDB from $495.00 to $500.00 and gave the company a “buy” rating in a report on Thursday, March 28th. Stifel Nicolaus reissued a “buy” rating and set a $435.00 target price on shares of MongoDB in a research report on Thursday, March 14th. Needham & Company LLC reaffirmed a “buy” rating and issued a $465.00 price target on shares of MongoDB in a report on Friday, May 3rd. Citigroup raised their price objective on MongoDB from $515.00 to $550.00 and gave the stock a “buy” rating in a research report on Wednesday, March 6th. Finally, Loop Capital started coverage on MongoDB in a research report on Tuesday, April 23rd. They set a “buy” rating and a $415.00 price objective on the stock. Two analysts have rated the stock with a sell rating, three have assigned a hold rating and twenty have assigned a buy rating to the company. Based on data from MarketBeat, MongoDB has a consensus rating of “Moderate Buy” and an average target price of $443.86.

View Our Latest Research Report on MongoDB

Insider Buying and Selling at MongoDB

In related news, CEO Dev Ittycheria sold 17,160 shares of the business’s stock in a transaction that occurred on Tuesday, April 2nd. The shares were sold at an average price of $348.11, for a total value of $5,973,567.60. Following the completion of the transaction, the chief executive officer now owns 226,073 shares in the company, valued at approximately $78,698,272.03. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is accessible through the SEC website. In related news, CAO Thomas Bull sold 170 shares of the business’s stock in a transaction that occurred on Tuesday, April 2nd. The shares were sold at an average price of $348.12, for a total transaction of $59,180.40. Following the completion of the sale, the chief accounting officer now directly owns 17,360 shares of the company’s stock, valued at $6,043,363.20. The transaction was disclosed in a legal filing with the SEC, which is available at this link. Also, CEO Dev Ittycheria sold 17,160 shares of the stock in a transaction that occurred on Tuesday, April 2nd. The shares were sold at an average price of $348.11, for a total transaction of $5,973,567.60. Following the completion of the transaction, the chief executive officer now owns 226,073 shares in the company, valued at $78,698,272.03. The disclosure for this sale can be found here. In the last 90 days, insiders sold 46,802 shares of company stock worth $16,514,071. 4.80% of the stock is currently owned by corporate insiders.

MongoDB Company Profile

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

Read More

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Article originally posted on InfoQ. Visit InfoQ

According to Camilla Montonen, the challenges of building machine learning systems have to do mostly with creating and maintaining the model. MLOps platforms and solutions contain components needed to build machine systems, but MLOps is not about the tools; it is a culture and a set of practices. Montonen suggests that we should bridge the divide between practices of data science and machine learning engineering.

Camilla Montonen spoke about building machine learning systems at NDC Oslo 2023.

Challenges that come with deploying machine learning systems to production include how to clean, curate and manage model training data, how to efficiently train and evaluate the model, and how to measure whether or not the model continues to perform well in production, Montonen said. Other challenges are how to calculate and serve the predictions the model makes on new data, how to handle missing and corrupted data and edge cases, how and when to efficiently re-train this model, and how to version control and store these different versions, she added.

There is a set of common components that are usually part of a machine learning system, Montonen explained: a feature store, an experiment tracking system so that data scientists can easily version the various models that they produce, a model registry or model versioning system to keep track of which model is currently deployed to production, and a data quality monitoring system to detect when some issues with data quality might arise. These components are now part of many MLOps platforms and solutions that are available on the market, she added.

Montonen argued that the tools and components do solve the problems for the systems they were designed for, but often fail to account for the fact that in a typical company, the evolution of a machine learning system is governed by factors that are often far outside of the realm of technical issues.

MLOps is not about the tools, it’s about the culture, Montonen claimed. It is not about just adding a model registry or a feature store to your stack, but about how the people who build and maintain your system interact with it, and reducing any and all friction points to a minimum, as she explained:

This can involve everything from thinking about git hygiene in your ML code repositories, designing how individual components of pipelines should be tested, thinking about how to keep feedback loops between data science experimentation environments and production environments, and maintaining a high standard of engineering throughout the code base.

We should strive towards bridging the divide between the practice of data science, which prioritizes rapid experimentation and iteration over robust production quality code, and the practice of machine learning engineering, which prioritizes version control, controlled delivery and deployment to production via CI/CD pipelines, automated testing and more thoughtfully crafted production code that is designed to be maintained over a longer period of time, Montonen said.

Instead of immediately adopting a bunch of MLOps tools that are more likely to complicate your problems instead of solving them, Montonen suggested going back to basics:

Begin with an honest diagnosis of why your machine learning team is struggling.

The largest gains in terms of data scientists’ development velocity and production reliability can be gained with a few surprisingly basic and simple investments into testing, CI/CD, and git hygiene, Montonen concluded.

InfoQ interviewed Camilla Montonen about building machine learning systems.

InfoQ: How well do the currently available MLOps tools and components solve the problem that software engineers are facing?

Camilla Montonen: Most big MLOps tooling providers grew out of projects started by engineers working on large language model training or computer vision model training, and are great for those use cases. They fail to account for the fact that in most small and medium sized companies that are not Big Tech, we are not training SOTA computer vision models; we’re building models to predict customer churn or help our users find interesting items.

In these particular cases, these ready-made components are often not flexible enough to account for the many idiosyncrasies that accumulate in ML systems as time goes on.

InfoQ: What’s your advice to companies that are struggling with deploying their machine learning systems?

Montonen: Find out what your machine learning team is struggling with before introducing any tools or solutions.

Is the code base complex? Are data scientists deploying ML pipeline code into production from their local machines, making it hard to keep track of which code changes are running in production? Is it hard to pinpoint what code changes are responsible for bugs that arise in production? Perhaps you need to invest in some refactoring and a proper CI/CD process and tooling.

Are your new models performing worse in online A/B tests compared to your production models, but you have no insight into why this happens? Perhaps you need to invest in a simple dashboard that tracks key metrics.

Having a diagnosis of your current problems will help you identify what tools will actually solve them and help you reason about tradeoffs. Most MLOps tools require some learning/maintenance/integration efforts so it is good to know that the problem you are solving with them is worth these tradeoffs.

Posted on mongodb google news. Visit mongodb google news

Victory Capital Management Inc. lifted its position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 43.9% in the fourth quarter, Holdings Channel reports. The institutional investor owned 55,818 shares of the company’s stock after acquiring an additional 17,042 shares during the quarter. Victory Capital Management Inc.’s holdings in MongoDB were worth $22,821,000 at the end of the most recent quarter.

Several other institutional investors have also recently made changes to their positions in MDB. Blue Trust Inc. increased its stake in MongoDB by 937.5% during the fourth quarter. Blue Trust Inc. now owns 83 shares of the company’s stock worth $34,000 after acquiring an additional 75 shares during the last quarter. Cullen Frost Bankers Inc. acquired a new stake in shares of MongoDB during the 3rd quarter worth about $35,000. AM Squared Ltd purchased a new stake in MongoDB in the 3rd quarter valued at about $35,000. Huntington National Bank lifted its position in MongoDB by 279.3% in the third quarter. Huntington National Bank now owns 110 shares of the company’s stock valued at $38,000 after buying an additional 81 shares during the last quarter. Finally, Parkside Financial Bank & Trust boosted its stake in MongoDB by 38.3% during the third quarter. Parkside Financial Bank & Trust now owns 130 shares of the company’s stock worth $45,000 after buying an additional 36 shares during the period. Hedge funds and other institutional investors own 89.29% of the company’s stock.

MongoDB Stock Down 0.6 %

MDB stock opened at $353.20 on Thursday. The company’s 50 day moving average is $366.16 and its two-hundred day moving average is $391.13. MongoDB, Inc. has a twelve month low of $244.85 and a twelve month high of $509.62. The company has a current ratio of 4.40, a quick ratio of 4.40 and a debt-to-equity ratio of 1.07. The stock has a market cap of $25.72 billion, a P/E ratio of -142.42 and a beta of 1.19.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its quarterly earnings results on Thursday, March 7th. The company reported ($1.03) EPS for the quarter, missing the consensus estimate of ($0.71) by ($0.32). MongoDB had a negative return on equity of 16.22% and a negative net margin of 10.49%. The company had revenue of $458.00 million for the quarter, compared to analyst estimates of $431.99 million. Research analysts expect that MongoDB, Inc. will post -2.53 earnings per share for the current fiscal year.

Wall Street Analysts Forecast Growth

Several equities analysts have recently commented on MDB shares. JMP Securities restated a “market outperform” rating and issued a $440.00 price objective on shares of MongoDB in a report on Monday, January 22nd. DA Davidson upgraded MongoDB from a “neutral” rating to a “buy” rating and upped their price target for the stock from $405.00 to $430.00 in a research note on Friday, March 8th. Citigroup lifted their price objective on MongoDB from $515.00 to $550.00 and gave the company a “buy” rating in a research note on Wednesday, March 6th. Needham & Company LLC reissued a “buy” rating and set a $465.00 target price on shares of MongoDB in a research note on Friday, May 3rd. Finally, Redburn Atlantic reiterated a “sell” rating and set a $295.00 price target (down from $410.00) on shares of MongoDB in a research note on Tuesday, March 19th. Two analysts have rated the stock with a sell rating, three have issued a hold rating and twenty have issued a buy rating to the company’s stock. According to MarketBeat, the company presently has an average rating of “Moderate Buy” and a consensus price target of $443.86.

Read Our Latest Research Report on MongoDB

Insider Transactions at MongoDB

In related news, Director Dwight A. Merriman sold 6,000 shares of the company’s stock in a transaction dated Friday, May 3rd. The stock was sold at an average price of $374.95, for a total value of $2,249,700.00. Following the completion of the sale, the director now directly owns 1,148,784 shares of the company’s stock, valued at approximately $430,736,560.80. The sale was disclosed in a legal filing with the SEC, which can be accessed through the SEC website. In other MongoDB news, Director Dwight A. Merriman sold 6,000 shares of the business’s stock in a transaction that occurred on Friday, May 3rd. The stock was sold at an average price of $374.95, for a total value of $2,249,700.00. Following the completion of the sale, the director now owns 1,148,784 shares in the company, valued at $430,736,560.80. The sale was disclosed in a document filed with the SEC, which is available through this link. Also, Director Dwight A. Merriman sold 1,000 shares of the stock in a transaction that occurred on Wednesday, May 1st. The shares were sold at an average price of $379.15, for a total value of $379,150.00. Following the completion of the sale, the director now directly owns 522,896 shares of the company’s stock, valued at $198,256,018.40. The disclosure for this sale can be found here. Over the last 90 days, insiders have sold 46,802 shares of company stock worth $16,514,071. Corporate insiders own 4.80% of the company’s stock.

MongoDB Company Profile

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

Further Reading

Want to see what other hedge funds are holding MDB? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for MongoDB, Inc. (NASDAQ:MDB – Free Report).

Receive News & Ratings for MongoDB Daily – Enter your email address below to receive a concise daily summary of the latest news and analysts’ ratings for MongoDB and related companies with MarketBeat.com’s FREE daily email newsletter.

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

The worldwide data management software market is massive (According to IDC, the worldwide database software market, which it refers to as the database management systems software market, was forecasted to be approximately $82 billion in 2023 growing to approximately $137 billion in 2027. This represents a 14% compound annual growth rate). At MongoDB we are transforming industries and empowering developers to build amazing apps that people use every day. We are the leading developer data platform and the first database provider to IPO in over 20 years. Join our team and be at the forefront of innovation and creativity.

We have an incredible opportunity for a transformational leader to join the MongoDB Technical Services team, leading the escalation management function for the Americas. This will be a player-coach role that oversees the team that ensures our customers and prospects are able to successfully run MongoDB at scale and helps them navigate any “bumps in the road” they may encounter.

We are looking to speak to candidates who are based in Toronto, Canada for our hybrid working model.

Typical responsibilities of the team include:

• Managing critical, “seconds matter” production down incidents to rapid closure
• Overseeing post-incident root cause analyses, including ongoing continuous improvement in broader Technical Services organization to prevent future recurrence
• Collaborating with sales, customer success and other field teams on addressing issues which may be impeding expansion of use within an account
• Partnering with managers and engineers within the organization to proactively prevent escalations by aligning the right resource to the right activity at the right time
• Collaborating with the product management and engineering teams to address issues which necessitate attention from those teams (including advocating for feature requests, where appropriate)

This is a role with a tremendous amount of customer interface. Our customers are the best and brightest in the business and they have great expectations of our products and our company. If you are the type of person who enjoys helping customers, managing complex and fast-moving situations, and being celebrated for “saving the day,” this is the job for you. 

Required

• 8+ years experience in a highly technical, post-sale role at a software company in either a Customer Support or Professional Services role
• Ability to support on-site visits with customers, and competently manage a presentation to a group of up to 30 people
• Prior people management experience
• Experience managing escalations at scale (multiple escalations per week spanning global resources), as well as managing cross-functional response (sales, support, professional services, engineering)

Desirable

• Prior experience as a software engineer is desirable, as MongoDB is a highly technical product and successful candidates typically are comfortable programming, writing queries, and operating in a command shell
• Prior work at a database company, specifically in the NoSQL space, or a similar highly concurrent distributed system used in production architectures
• Born-in-the-cloud SaaS experience. IaaS or PaaS highly desirable
• Experience producing management dashboards and scorecards using tools like Tableau

Success Measures:

• Within 30 days
  • Complete our new hire technical training program; be able to speak confidently about our total portfolio of products
  • Be able to navigate our core products at a beginner level
• Within 60 days
  • Understands our global follow-the-sun processes and escalation processes
  • Is able to handle a minor process escalation independently
  • Begin conducting regular 1on1s with staff of escalation managers as well as other managers within Technical Services
  • Conducts listening sessions amongst global leaders about areas for improvement in the escalation management program
• Within 90 days
  • Demonstrates ability to handle more complex escalations and emergency situations
  • Implements effective bi-directional communication between the escalation management team and the Americas management team
• At 120 days and onward: 
• • Has a roadmap to continue scaling technical knowledge in the product commensurate with role needs
  • Conducts an on-site with a marquee customer
  • Produces growth plans for the team of escalation managers
  • Begins production of a strategic roadmap for escalation management as an overall program; inventories tools and assets and begins planning requests-for-investment to scale capabilities

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB is an equal opportunities employer.

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

MongoDB, a leading technology corporation, have announced the appointment of Abi Nourai as the new Regional Vice President for Australia and New Zealand (ANZ). Nourai will spearhead MongoDB’s thriving ANZ sections, coordinating with its regional leadership team on determining and implementing the firm’s sales growth and development strategies. His responsibilities will also encompass overseeing talent acquisition and enhancing brand recognition.

Having served as MongoDB’s Regional Director for the UK and Ireland for almost five years, Nourai is stepping into this role following continuous excellence in leading regional sales teams. His 15-years’ worth of experience in the fields of engineering and sales in both the UK and Australia, combined with his previous leadership roles at influential firms such as Medallia and Open Kernel Labs, are certain to boost his stewardship of the ANZ division.

During his previous tenure as MongoDB’s Regional Director, Nourai headed teams that encompassed and fostered growth among MongoDB’s largest UK-based customers, spanning sectors like financial services, telecommunications, retail, digital natives, and the public sector.

Nourai’s promotion has been hailed by MongoDB’s senior leadership. Simon Eid, MongoDB’s Senior Vice President for the APAC region commented, “Abi has demonstrated exceptional skill and dedication in developing his team and building a strong culture of pipeline generation to fuel individual and regional success.” Eid further expressed his anticipations regarding Nourai’s tenure, “I look forward to seeing Abi achieve new successes in this role and drive strong growth in ANZ as we make AI more accessible to customers.”

In his new role, Nourai will be instrumental in accelerating the adoption of generative AI within businesses by linking them to comprehensive programs like MongoDB’s AI Applications Program (MAAP). MAAP provides customers with an accessible and intuitive framework for understanding how to build AI applications.

Commenting on his appointment, Nourai conveyed his confidence in MongoDB’s continued success and his excitement to work with a high-performing team that is committed to success. “MongoDB is empowering developers across industries to build amazing apps that people use every day, and I am confident that we will continue to be the platform of choice for businesses of all sizes,” he says. “I look forward to continuing to help our customers realise their business strategy through software.”

Nourai’s deep understanding of MongoDB’s products, like MongoDB Atlas and Vector Search, will provide customers a competitive edge in a rapidly evolving market, ensuring MongoDB remains the platform of choice even in an ever-changing global IT landscape.

Article originally posted on mongodb google news. Visit mongodb google news

Posted on mongodb google news. Visit mongodb google news

The worldwide data management software market is massive (According to IDC, the worldwide database software market, which it refers to as the database management systems software market, was forecasted to be approximately $82 billion in 2023 growing to approximately $137 billion in 2027. This represents a 14% compound annual growth rate). At MongoDB we are transforming industries and empowering developers to build amazing apps that people use every day. We are the leading developer data platform and the first database provider to IPO in over 20 years. Join our team and be at the forefront of innovation and creativity.

About the role

We’re looking for a hardworking, driven individual with superb energy, passion and initiative for new business acquisition. The Enterprise Account Executive role focuses exclusively on formulating and executing a sales strategy within an assigned territory, resulting in revenue growth and new customer acquisition.

This role will be based out of Seattle.

MongoDB’s Sales Culture

MongoDB is always developing and innovating — not only in our technology, but also in our sales go-to-market strategy. Our sales leadership is committed to building the best salesforce in technology. This means, inspiring and enabling success for everyone on the team. We not only equip you to be successful and close deals, but we want your feedback and input on how we can continue to “Think Big and Go Far.” As a crucial part of the Sales team at MongoDB, you will have access to a lucrative market and learn how to sell from some of the most successful sales leaders in the software industry.

What you will be doing

• Proactively, identify, qualify and close a sales pipeline 
• Strategically prospect into CTOs, Engineering/IT Leaders, & technical end users.
• Build strong and effective relationships, resulting in growth opportunities
• Partner with our Solution Architects and work closely with the Professional Services team to achieve customer satisfaction
• Work closely with the enterprise ecosystem partner sales and channel partner to maximize deal sizes
• Participate in our sales enablement trainings, including our comprehensive Sales Bootcamp, sophisticated sales training, and leadership and development programs

What you bring to the table

• 5+ years field experience of quota-carrying experience in a fast-paced and competitive market with a focus on closing net new logos. Demonstrated ability to open new accounts and run a complex sales process 
• A proven track record of overachievement and hitting sales targets
• Ability to articulate the business value of complex enterprise technology
• Skilled in building business champions
• Fluent in English
• Must live in territory
• Driven and competitive. Possess a strong desire to be successful
• Previous Sales Methodology training (e.g. MEDDIC, SPIN, Challenger Sales)
• Familiarity with databases, develops and open source technology a plus

Why join now

• MongoDB invests 8x the industry average in development of each of our new hires & continuous career development
• Accelerators up to 30%
• Best in breed Sales trainings in MEDDIC and Command of the Message, including our comprehensive Sales Bootcamps and development programs
• New hire stock equity (RSUs) and employee stock purchase plan
• Generous and competitive benefits (parental leave, fertility & wellbeing support)
• Friendly and inclusive workplace culture – Learn more about what it’s like to work at MongoDB

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Article originally posted on mongodb google news. Visit mongodb google news