Think Big, Go Far with the MongoDB Customer Success Team

Customer Success Managers (CSMs) play an integral role at MongoDB by providing our customers with the tools and guidance they need to make an impact on the world with their applications. Our Customer Success team is made up of employees from all around the world and collectively speaks 10 languages. Stay tuned to learn more about how the team has grown over the years and get insights into what a day in the life of a CSM at MongoDB is really like.

Customer Success at MongoDB

For CSMs at MongoDB, every day is different. With a wealth of growth opportunities, a diverse set of customers, and the chance to play a role in the full lifecycle of a customer’s journey, CSMs have the opportunity to constantly innovate and develop creative solutions.

Here, Demarcus Lloyd and Paige Jornlin from the U.S.-based team share what it’s like to be a CSM at MongoDB.

Demarcus Lloyd, Senior Customer Success Manager, Austin

Photo of Demarcus Lloyd

Candidates often think you need to have a strong background in tech to be a CSM at MongoDB, but that’s not necessarily the case. I, for example, completed a college degree in chemistry and international studies, and in 2015, I was working full time in a molecular biology and pathology division of a lab. Initially, I was worried that without a computer science or engineering degree, the move to a technology company would be too difficult of an adjustment, but the opposite proved to be the case. I transitioned my career and joined a health IT company, where I was promoted to a Customer Success role within a year. That role opened doors for other opportunities and eventually led me to join the database space with MongoDB. My portfolio includes computer software and hardware companies, as well as retail, media, oil and gas, and even healthcare companies. This type of diversity in industries creates exposure to some really niche and interesting use cases for MongoDB.

One of my favorite success stories is about one of my customers that had major concerns around migrating from on-prem deployment to our cloud offering, Atlas, and sharding several terabytes of data for a customer-facing application. This hesitancy was delaying the company’s overall goal to offload from its data centers by 2022, which could implicate millions of dollars in cost savings. Knowing the team needed to see another successful customer example to move forward, I leveraged a key stakeholder relationship I built with another customer that had a very similar migration and sharding experience. The Lead Engineer agreed to provide me with their tech specs, success metrics, and migration experience to be shared back with the customer. By sharing this proof point, I was able to restore my customer’s confidence, and the company was able to migrate its preproduction workloads a month ahead of schedule and begin migrating production workloads in Q3, ultimately accelerating and de-risking an expansion.

When it comes to our team culture, the Customer Success organization is a collective of some of the most talented and selfless individuals I have had the opportunity to work with. For most of us that don’t come from a database background, we recognize that MongoDB can be challenging for beginners to learn. However, the individuals on the team all have strengths and weaknesses, and this is where the collaborative spirit pulls through. A lot of the playbooks, team enablement, and initiatives that are in place for the team are spearheaded by the CSMs themselves, with a focus on the betterment of the global team.

We also have a lot of team events. I manage our global happy hour sessions for all new hires every quarter to welcome them to the team. Recently, we had a trivia night as a team-building exercise. The questions ranged from films to history, music, and even Greek mythology. With the various age groups that make up the CSM team, it was interesting to see firsthand the generational familiarity across the topics. At times, it was really competitive, but it was a lot of fun.

Paige Jornlin, Customer Success Team Lead, New York City

Photo of Paige Jornlin

When I first joined MongoDB in May 2018, I was blown away by the onboarding process. Right from day one, they guide you through your ramp-up and make sure you have everything you need to feel comfortable talking to these really technical customers. I also was paired up with a “buddy” at MongoDB to help me with all of my questions during my first few weeks, and I attended our incredible sales boot camp. Since 2018, we’ve only made improvements to these programs. At the beginning of COVID-19, we built out a Customer Success boot camp to take these concepts they’re learning about throughout their ramp-up plan and let them hear directly from the experts. I was certainly impressed two-and-a-half years ago when I joined, but it’s changed drastically, and we have a huge focus on making sure that any new hire has all the tools and support available to really get comfortable with it.

The Customer Success team takes a really powerful, impactful product and jumps in to expedite and ensure a customer’s success. We offer a really deep level of guidance and partner closely with our clients as they work toward all of their upcoming objectives. We like to think of ourselves as an extension of their team. We also focus on making sure customers can take advantage of the whole ecosystem of tools they have available to them and on ensuring that their experience is seamless.

Every day in my role is different. One day, I might be meeting with our Sales leaders from the various regions I support to strategize on how to best support accounts and de-risk them, while on another day, I might be working with my team to drive toward their metrics and career goals. I’m also really passionate about bringing the team together for enablement and mindshare of what’s going well and what the team might be struggling with.

Our team culture is one of the things that excites me the most. Having a team of people I want to be around who are extremely intelligent, challenge me, and make me feel super comfortable is of the utmost importance to me. Because of COVID-19 and our office closure, I ate lunch by myself for the first time after two years in my role here, and it felt so weird because this is such a tightly knit team. We’re always doing things together — constantly looking to collaborate on projects or even just hang out. The team culture has been one of my favorite parts of working at MongoDB. Our team is also really diverse, and people’s differences are embraced. Without having that diversity of background, thought, race, sex, and so forth that exists on our team, we wouldn’t be able to innovate, challenge the norm, or think about different ways of doing things as much as we do.

Interested in pursuing a career at MongoDB? We have several open roles on our teams across the globe, and would love for you to build your career with us!

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Built With MongoDB: Stoovo

When Hantz Févry and Pierre Mombeleur first met, they wouldn’t have guessed they’d one day become co-founders. As students at the same high school in Haiti, they weren’t particularly close. There was no noticeable chemistry when they faced off in a music competition either, and they maintained only a passing acquaintance on Twitch. But both attended college in New York. At the time, Hantz’s checking account was constantly overdrawn, and it was harder than he thought to pick up short-term work. He and Pierre started talking about ways to help gig workers maximize their income by providing intelligence about how and when to use platforms such as Uber and DoorDash. They kept working on that idea even as both got jobs at Google. In 2015, the pair met Semih Korkmaz, who became their CTO, and in 2018 they launched Stoovo , an app to help gig workers maximize their income. Stoovo now covers 6,000 platforms used by gig workers to find jobs, and has 17 employees. Just last week, the company announced that it had raised a $2.4 million seed round from 500 Startups, Alpana Ventures, Plug and Play Ventures and Watertower Ventures. In this edition of #BuiltWithMongoDB, we talk to the Stoovo team about building for the gig economy, immigrant entrepreneurship, and their experience growing with MongoDB for Startups . How did you keep Stoovo going while you were at Google? Hantz: There is a big misconception among venture capitalists that you need to be working full-time on your startup. That is not true in the case of immigrants. I’m an immigrant, and don’t have my aunt’s couch to sleep on. I don’t have this famous garage in Silicon Valley. So I basically had to work two full-time jobs. After working until 5 pm for Google, I started working with Semih, often until three or four in the morning. What is your vision for Stoovo for the next few years? Hantz: You have three main players in the gig economy: the platforms, the requester, and the supplier. The problem is that the worker, or the supplier, is left completely alone, because the platform is optimizing to meet the demand of the requester. We want to shift that optimization to help workers maximize their income for each hour worked. We want to give them financial stability through income boosting and income smoothing. Without Stoovo, workers are on their own trying to figure out if their time is better spent on DoorDash or Uber Eats or Postmates. They realize that if they blindly follow the direction of those platforms, they may not make enough money. The average gig worker uses three platforms, and shuffles arbitrarily between them, or relies on word of mouth, which is not really optimal. We tell gig workers which platform is best for them at any given time, and when to take a break. We go even further to help with financial management. We envision another type of banking for gig workers, where we not only store the money, but we’re better adapted to their realities. How does the typical gig worker use Stoovo? Hantz: We have one user, Lionel, who is an actor and does gig work to help ends meet. Without Stoovo, he was trying to use Uber sometimes, sometimes Postmates. Sometimes he made money, sometimes he didn’t. With Stoovo, he knows exactly how to shift between his acting career, and exactly what to do and how many hours he needs to work. Another user might rely more on the finance side. We have one user, Megan, who relies on the card for a cash advance. At first we were thinking that gig workers would use the cash advance to pay for gas. But when we looked at the transactions, a lot of them were fast food. Megan explained that gig workers often don’t even have a sufficient cash flow to buy food. This is why we built the two products together. We help you plan your day to maximize your income and we provide you with cash flow if needed. How have you built your tech stack to enable this? Semih: Stoovo is a very distributed system and works with many different partners, especially geospatial data providers, so our stack is a little bit heterogenous. On the application side we use Node.js, and MongoDB is our database platform. On top of that, of course, we have lots of Python scripts running. How did you select MongoDB as your database solution? Semih: We did a few prototypes with relational databases, but there were so many changes – not only on our side, but at our data partners – that we weren’t able to stick with a schema. And changing the schemas in our applications was almost completely eliminating our chances to work in an agile manner. We were really impressed with some of the capabilities of the graph databases, but you can always represent a graph in a document format. That’s when we realized we would go with NoSQL. After that, it was easy to choose MongoDB. What has it been like scaling with MongoDB? Semih: MongoDB Atlas made our life much, much easier. If you’re a startup, when you first start with a database, you want to start small. You have to keep your costs under control and your maintenance low. Then you need to scale up, and you see many startups fail to do this. Thanks to Atlas’ sharding system and its scaling system, which is automatically handling each replica’s scaling, we were having no problems at all with this, and we were easily able to scale. What are some of the features of MongoDB you find most valuable? Semih: We are rapidly developing many machine learning models, and they can be changing all the time. Those can create a new collection or change an existing collection, and change the access patterns. When this happens, indexing becomes really important. MongoDB Atlas helps us in two ways. One is that it gives us real-time alerts when our interaction pattern has changed. And, it suggests what the index should be. Then we can say, “Yes, please go ahead,” and apply this index on a rolling basis, and that makes life really easy for us. There is so much happening in the gig economy. What are your thoughts on how Stoovo fits into the future of the sector? Pierre: There’s an increasing amount of pressure for platforms to make sure workers’ rights are respected. We want companies to really give workers context into their work and be able to use data to their advantage, rather than holding onto the data and only using it themselves. Hantz: If you talk to someone from Gen Z, they don’t see themselves working for a big corporation from nine to five. There will also be a lot of automation and a lot of displacement of jobs. So a lot of people will jump into the gig economy, and they’ll need two things: They’ll need to make sure they earn enough, and they will need a financial system built for gig workers. This is exactly where we’re trying to position Stoovo. Looking to build something cool? Get started with the MongoDB for Startups program.

March 16, 2021
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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024