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 SoftBank 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, ClickHouse Cloud as the primary analytical store, CDC off Postgres and MongoDB source systems, a long tail of third-party sources, and Snowflake, Redshift, BigQuery, 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.
*First, how our own team works.* 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.
*Second, how the rest of the company works.* 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.
*What you’ll do:*
*What you should bring:*
*Nice to have:*
*What success looks like:*
These are weeks, not quarters. That’s the point, and it’s possible because you won’t be carrying the platform while you do it. The models and pipelines have owners, so your time goes into building rather than inheriting.
*Week 1* — you’ve shipped. A small agent-assisted change, merged to production. You learn our stack by building in it.
*Week 2* — the first agentic workflow is running against production. Agent-authored changes moving through a review path you designed, with the CI and guardrails that make merging them safe.
*Week 4* — engineers other than you are using it daily, the evaluation layer is catching regressions before a human does, and the first agentic analytics capability is in front of real users outside the data team, rough edges and all.
*Week 8* — agent-initiated is the default path for at least one entire class of data work, the patterns are documented well enough that adoption no longer depends on you being in the room, internal agentic analytics is live for at least one department with measurement behind it, and the rate at which we add trustworthy data capability is no longer bounded by the size of the data team.
Even if you do not meet all the requirements above, we still encourage you to apply for this position. While we try to be thorough with our prerequisites, not everything about you as a candidate can be condensed into a list of bullet points. What do you have to lose?
*Fountain offers an incredibly unique work environment.*We employ a diverse team all over the world. Each Fountaineer is given the freedom to do their best work from wherever they choose. We also understand the importance of in-person connections and hold in-person meetings with your team and meet annually as an organization to build our relationships and focus on the future of moving Fountain Forward.
The benefits we offer in the United States include competitive health plans and a retirement plan. Some Fountain-wide perks offered to all employees across the globe include a flexible vacation policy, paid holidays, monthly lunch stipends, annual allowances for ongoing education related to your profession and career advancement, along with home office, cell phone, and wellness reimbursements. Fountain is a global employer, so some benefit offerings will vary from country to country.
Fountain is proud to be an equal opportunity workplace. We welcome applicants of any educational background, gender identity and expression, sexual orientation, religion, ethnicity, age, socioeconomic status, disability, and veteran status.
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