Applied Forward Deployed Engineer (Post-Sales)

About Monte Carlo Monte Carlo is the agent trust platform that autonomously observes, fixes, and continuously improves agents and the data that powers them — so enterprises can scale trust at the speed of AI. Bayes, our AI teammate, does the work: detecting, triaging, troubleshooting, and fixing issues in AI agents and the data powering them. The result is self-improving agents that get better with every fix instead of drifting. More than 400 enterprises, including Amazon, PepsiCo, CNN, Nasdaq, and JetBlue, rely on Monte Carlo. Learn more at montecarlo.ai https://montecarlo.ai. THE ROLE We're hiring an Applied Forward Deployed Engineer (Post-Sales) to own the technical side of every new customer from the moment the deal closes until they're live and getting value. You'll write production code in customers' Snowflake and Databricks environments, integrate Monte Carlo with their real pipelines, and become the technical advisor they call before they escalate. This isn't support and it isn't a CSM role: your metric is whether the customer is live and consuming. It's an early hire into a new function, so you'll help write the FDE playbook, not inherit one. Remote (US), with roughly 25% travel to customers. WHAT YOU'LL DO - Own deployment from day one post-close: monitors, alerts, and integrations configured for the customer's actual environment. - Write production-quality Python, SQL, and API integrations tailored to customer pipelines, and clear whatever stands between a signed contract and a fully operational deployment. - Drive customers to consumption. You're accountable for active usage and measurable value, not just go-live. - Expand adoption beyond the first champion across teams, data assets, and use cases. - Feed what you see in the field back to Product and Engineering so it shapes the core product instead of turning into one-off work. - Shape the FDE playbook as an early member of the team. WHAT WE'RE LOOKING FOR - Data platform depth. 5+ years building on Snowflake, Databricks, or a modern cloud warehouse as the person who designed and built it, not an end user. Production Python and SQL, and comfortable wiring up REST APIs in someone else's environment. - Post-sale ownership. You've owned technical outcomes for enterprise customers after the contract: implementation, solutions or forward deployed engineering, technical onboarding. You measure yourself on go-live and adoption. - Range with people. You can run a working session with a room of data engineers and brief a VP in the same week. - Builder in ambiguity. You've operated without a playbook and built the structure yourself. You create motion instead of waiting for direction. - Nice to have. dbt, Airflow, Fivetran, or Looker; data quality, pipeline monitoring, or incident response; a background in data observability or an adjacent category. THIS IS NOT FOR YOU IF - You want a support queue or a defined playbook to follow. - You've only worked pre-sale and never owned what happens after the contract is signed. - Your data experience is all internal, with no customer-facing context. - You'd rather advise on the stack than write code in it. #LI-REMOTE #BI-REMOTE WHY MONTE CARLO - We created the data observability category, and we're doing it again with agent trust https://montecarlo.ai/agent-trust — you'll build where the market is forming, not where it's settled - Series D, $236M raised, backed by Accel, Redpoint, Notable Capital, ICONIQ Growth, and Salesforce Ventures - Customers include HubSpot, Fox, Nasdaq, Toast, and Mercado Libre — your work ships to enterprises with real stakes - Snowflake Partner of the Year and a verified connector in Anthropic's Claude AI directory - Remote-first by design since day one, and recognized as a Best Workplace for it - Competitive compensation, equity, and a remote-first environment. Come As You Are Equality is a core tenet of Monte Carlo's culture. We are committed to building an inclusive global team that represents a variety of backgrounds, perspectives, beliefs, and experiences. Monte Carlo is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We are proud to be recognized for our world-class employee experience: Monte Carlo Named 2025 Databricks Data Governance Partner of the Year https://www.montecarlodata.com/blog-2025-databricks-data-governance-partner-of-the-year/?utm_source=chatgpt.com We were recently recognized as the #1 Data Observability Platform by G2 for the 4th consecutive quarter. See our G2 reviews here! https://www.g2.com/reports/grid-report-for-data-observability-spring-2025.embed?featured=monte-carlo&secure%5Bgated_consumer%5D=7d02ec0a-326a-40fa-8a44-fab49f67c5f1&secure%5Btoken%5D=6b3c29d18ea50ae0005295b5c63994f97c01cae81bbd3f9ea6abff73c40fde51&utm_campaign=gate-2063400 Monte Carlo Named to G2's Best Software Products of 2026 https://www.montecarlodata.com/blog-monte-carlo-g2-best-software-product-of-2026/ Monte Carlo was featured on Database Trends and Applications (DBTA’s) Trend-Setting Products for 2025! https://www.dbta.com/Editorial/Trends-and-Applications/Trend-Setting-Products-in-Data-and-Information-Management-for-2025-167115.aspx We are super proud to be named the 2026 Best Place to Work by Built In! https://builtin.com/awards/us/2026/best-places-to-work Beware of Imposter Recruiters and Job Scams - All official communication from our recruiting team will come from an @montecarlodata.com http://montecarlodata.com email address. - We will never ask candidates to provide sensitive personal information (such as bank details, social security numbers, or payment) at any stage of the recruitment process. - We will never request payment for equipment, training, or application processing. - Our open positions are always listed on our official careers page: https://jobs.ashbyhq.com/montecarlodata. If you are contacted by someone claiming to represent Monte Carlo but you’re unsure of their legitimacy, please reach out to us directly at recruiting@montecarlodata.com before sharing any personal information.