Applied Forward Deployed Engineer (FDE) - Monte Carlo

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.

The Role

We're hiring an Applied Forward Deployed Engineer 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.

• Location: 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, clearing 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 field insights 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 experience, plus comfort wiring up REST APIs in customer environments.
• Post-sale ownership: Experience owning technical outcomes for enterprise customers after contract signing (implementation, solutions engineering, forward deployed engineering, or technical onboarding). You measure success on go-live and adoption.
• Range with people: Ability to run a working session with data engineers and brief an executive in the same week.
• Builder in ambiguity: Comfort operating without a defined playbook, creating structure and momentum independently.
• Nice to have: Experience with dbt, Airflow, Fivetran, or Looker; expertise in data quality, pipeline monitoring, or incident response; background in data observability or adjacent fields.

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 post-contract execution.
• Your data experience is entirely internal with no customer-facing context.
• You'd rather advise on tech stacks than write production code in them.

Why Monte Carlo

• Pioneering the Field: We created the data observability category and are doing it again with agent trust.
• Strong Backing: Series D, $236M raised, backed by Accel, Redpoint, Notable Capital, ICONIQ Growth, and Salesforce Ventures.
• Enterprise Scale: Customers include HubSpot, Fox, Nasdaq, Toast, and Mercado Libre.
• Ecosystem Recognition: Snowflake Partner of the Year and verified connector in Anthropic's Claude AI directory.
• Remote-First: Remote-first culture since day one, recognized as a top workplace.
• Total Compensation: Competitive base salary, equity, and comprehensive remote work support.