The Role

We're building a new kind of post-sale technical role. Not a Support Engineer. Not a traditional CSM. An Applied Forward Deployed Engineer, someone who takes ownership the moment a deal closes and doesn't let go until the customer is fully live, deeply adopted, and driving real value from Monte Carlo.

This is a post-sale role inside our GTM organization, focused entirely on deployment, adoption, and getting customers to consumption. You'll work closely with Customer Success and Account teams, but your metric is technical - is this customer live, and are they getting value?

What You'll Do

• Own onboarding and deployment from day one post-close - getting customers live on Snowflake, Databricks, and adjacent stack components with the right monitors, alerts, and integrations configured for their environment.
• Drive customers to consumption - you're accountable for ensuring they're actively using what they bought and realizing measurable value, not just technically deployed.
• Write production-quality code where needed: custom integrations, API-based automations, SDK implementations, and data quality rule deployments tailored to the customer's actual pipelines.
• Unblock customers fast - diagnosing deployment issues, resolving edge cases, and removing whatever stands between a signed contract and a fully operational Monte Carlo environment.
• Build adoption depth beyond the initial champion - helping customers expand usage across teams, data assets, and use cases to drive long-term stickiness.
• Become the technical advisor customers call before they escalate - shaping how they operationalize data observability and growing into a trusted extension of their data team.
• Feed deployment and adoption signals back to Product and Engineering - you'll have the clearest view of what's working in production and where customers get stuck.
• Help define what great post-sale technical execution looks like as an early FDE hire - you'll shape the playbook.

What We're Looking For

Data Stack Depth

5+ years building on Snowflake, Databricks, or modern cloud data warehouse environments - not as an end user, as someone who designs, builds, and debugs on top of them. Familiarity with the tools that surround the warehouse - dbt, Airflow, Fivetran, Looker, or similar - is a strong plus.

Production Code

Comfortable writing Python and SQL and working with REST APIs in customer environments. You solve problems with code, not slides.

Customer Presence

You've owned technical relationships with enterprise customers. You can run a room of data engineers and give a crisp status update to a VP in the same week without switching personas.

Post-Sale Ownership

You've been the person accountable for getting customers from signed contract to live and adopted - whether in implementation, technical onboarding, solutions consulting, or a similar post-sale role. You know what it takes to drive consumption, not just deployment.

Ambiguity Tolerance

You've worked in environments where the playbook didn't exist yet. You didn't wait for one - you built it.

Data Quality / Observability (Strong Plus)

Familiarity with data quality concepts, pipeline monitoring, or incident response in data environments.

Education:

Bachelor's degree in computer science, data science, engineering, economics, business analytics, or a related field. What you've built and who you've helped matters more than where you studied.

This Is Not For You If

• You measure success by go-live, not by consumption.
• You prefer deep, isolated engineering work over customer interaction.
• You're uncomfortable owning outcomes after handoff from Sales.
• You need a fully defined playbook before you can move.

This role will frustrate you if any of those are true. It's built for engineers who care about outcomes, not just delivery.

Why Monte Carlo

• Category leader in data observability - a problem that only gets harder as AI raises the stakes for data reliability.
• Joining as an early FDE hire means real influence on how post-sale technical execution scales.
• Tight partnership with Customer Success, Product, and Engineering - no silo, no hand-off culture.
• Customers are data-sophisticated: you'll work with engineers who push back, which keeps the work sharp.
• Competitive compensation, equity, and a remote-first environment with ~25% travel for customer engagement.

#LI-REMOTE

#BI-REMOTE