Note: The job is a remote job and is open to candidates in USA. Fountain sells agentic software and is building its own data platform and agentic capabilities. The Senior Data Engineer, Agentic Systems will design and build agent-driven development workflows, evaluation systems, and analytics capabilities while applying strong data engineering practices. The role also drives adoption across engineering, product, finance, customer-facing, and support teams.
Responsibilities
- Design and build the agentic development workflow for the data team. Agent-authored models and pipeline changes, the context and tooling those agents operate through, PR and review patterns, CI that catches what agents get wrong, the guardrails that make it safe to run against production, and the standards that make any of it repeatable across a team
- Push that reach upstream into the product. Build the context and tooling that shape how features get conceived and built in the first place, and verify data architecture while changes are in development. Most data problems are cheapest to fix at the root, before a dev PR lands, which means working inside Engineering and Product rather than downstream of them
- Build the evaluation layer. Regression suites, correctness checks, lineage awareness, and observability on the agents themselves. Nobody adopts an agentic workflow they can’t verify, so this is foundational rather than a follow-up
- Build agentic analytics capability for the company. Semantic context, skills, MCP surfaces, access controls, and query and cost guardrails that let non-technical teams get answers they can act on, along with the honest scoping of what the system won’t answer
- Drive adoption. Work alongside the engineers on the data team and stakeholders across GTM, Finance, Product, and Support, teaching the patterns, watching where people get stuck, and closing the gap between what you built and how people actually work
- Stay hands-on in the platform. You won’t own the models and pipelines, but you’ll work in that codebase constantly, and you should be able to fix what you find
Skills
- 5+ years in data engineering, analytics engineering, or a closely related role, or equivalent depth arrived at another way
- Real depth in analytical data modeling and SQL. You can read a normalized transactional schema, design something that answers business questions without falling over, and spot when a generated model is subtly wrong
- Hands-on production experience with dbt and an orchestrator. Dagster is what we run; Airflow, Prefect, or Temporal transfers fine. You should know the operational reality of a large DAG, not just the syntax
- Strong Python and the software engineering habits that go with it: version control, testing, code review, CI/CD
- You have shipped agentic or LLM-powered systems to production. Tool and function calling, context and retrieval design, orchestrating multi-step workflows, and evaluating all of it. You should be able to walk through something you built, how you measured whether it worked, and where it failed. This is the requirement we care most about
- Sound judgment about data governance and PII in a multi-tenant environment. We handle applicant and worker data across 75+ countries, and exposing it through an LLM surface raises the stakes on getting access boundaries right
- You can bring people with you. This role changes how the data team, product engineers, and other departments do their work. That requires listening well, teaching patiently, and being persuasive without a title that makes anyone do anything
- Depth in a columnar or MPP warehouse, especially ClickHouse Cloud and ClickPipes; Snowflake, BigQuery, or Redshift also translates
- Building with Claude: the API, Claude Code, the Agent SDK, MCP servers, or subagent patterns
- AWS, Kubernetes, and infrastructure as code, enough to debug your own deployment
- Semantic layer and BI tooling (Omni, Looker, dbt Semantic Layer, Cube)
- LLM observability and evaluation tooling (Langfuse, Braintrust, or equivalent)
- Streaming and CDC internals (Debezium, Kafka, Kinesis)
- Having led a technical practice change on a team that was skeptical at the start
- HR tech, high-volume hiring, workforce management, or another domain with heavy operational data
Benefits
- Health Insurance
- Dental Insurance
- Paid Time Off (PTO)
- Paid Holidays
- Sick Leave
- Remote Work
- Professional Development
- Parental Leave
Company Overview