Senior Data Engineer, Agentic Systems

When you join the Fountain team, you become part of the leading enterprise solution for frontline workforce management. Fountain’s automated, customizable platform provides a seamless applicant experience for workers, while ensuring organizations can scale and manage their frontline talent.

We’ve helped hundreds of companies like UPS, CLEAR, Stitch Fix, GoPuff, Fetch, and sweetgreen to hire, onboard, and manage over 14 million workers in more than 75 countries.

In 2022, we closed $185M in our Series C, led by Soft Bank and B Capital.

Join our growing team of highly collaborative, ambitious, and forward-thinking Fountaineers as we empower our hundreds of customers and millions of frontline workers around the world.

Let’s elevate frontline work together.

Fountain sells agentic software. We intend to run on it too.

Our data platform is healthy and owned. dbt and Dagster on Kubernetes, Click House Cloud as the primary analytical store, CDC off Postgres and MongoDB source systems, a long tail of third‑party sources, and Snowflake, Redshift, Big Query, and object‑store lakes around the edges. Engineers on the team own those models and pipelines today, and they’ll keep owning them.

This role is different. You own the agentic buildout itself, in two halves.

We want the data team building agentically by default: agents that author and test models, extend pipelines, catch breakage and repair it, keep documentation and contracts honest. Someone has to design that system. The harnesses, the context and tooling agents work through, the review and CI workflows that make agent‑written code safe to merge, the evaluations that tell us whether the output can be trusted.

Other engineers own the models. You own the machinery that changes how they get built, and that machinery reaches upstream into the product, where most data problems actually start.

The same capability, pointed outward. A CSM, a finance analyst, a PM, or a support lead should be able to ask a real question and get a trustworthy answer without waiting in a queue. That means semantic context, skills and MCP surfaces, access boundaries, evaluation, and a clear‑eyed view of what the agent should refuse to answer. It also means working closely with the people who’ll use it, because tooling nobody adopts is worth nothing.

At its core this is still a data and analytics engineering job. You need enough depth in modeling, dbt, and orchestration to build credibly for engineers who do it all day, and to know when agent‑generated output is subtly wrong. The difference is what you’re accountable for: the leverage, not the DAG.

Be aware that this is as much a change problem as an engineering problem. Building the capability is half of it. Getting a team, and then a company, to genuinely work differently is the other half, and it’s the half that decides whether this role succeeds.

We’re moving quickly. This role is a bet that we can change how we work in weeks rather than quarters, and we’re staffing it accordingly.

• 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…