About this Sr. Data Engineer role at Clipboard
About Clipboard
Our mission is to uplift as many communities as possible. We do this through our app-based marketplace that connects healthcare professionals with the workplaces that need amazing workers. This enables hundreds of thousands of people to achieve financial stability for themselves and their families while providing essential care to millions of people across the U.S.
Founded in 2016, we are a remote-first team of over 1,000 people building a top Y-Combinator company and have been profitable since 2022. We’re the leader in Long-Term Care staffing and are rapidly expanding into Home Health, Hospitals, and more, meaning we have more work to do than people to do it, and are growing our team to support millions more people and their communities.
Data Engineering at Clipboard is deeply embedded in the business. Our exceptional engineers own the full software development lifecycle, from design and implementation through deployment and ongoing support. Engineers at Clipboard have real autonomy over their work and are expected to take full ownership of what they build. For Data Engineering, that ownership means the pipelines, models, and tooling you build are load-bearing for how operations, finance, product, and a growing set of AI-assisted workflows make decisions every day.
About the Role
We're looking for a Senior Data Engineer to join our Data Engineering team, which makes Clipboard's data and knowledge infrastructure reliable and well-governed for everyone who makes decisions with it, human analysts and AI agents alike.
You’ll build systems that make the data and definitions that matter most to the business easily accessible, like how we calculate net revenue, which shift statuses to commonly exclude, or what a "verified shift" means. All too often things like these live in people's heads, get rediscovered from scratch in every new analysis, and diverge across teams over time. You’ll develop workflows to enable capturing that meaning as governed, versioned artifacts (dbt semantic models, Snowflake views, structured knowledge files) so every customer can reuse it, regardless of whether that's a Hex project or a Claude agent.
This increasingly means building for AI as a first-class consumer. AI-assisted analytics is only as good as the data and knowledge context quality underneath it, and you'll design and build the systems that close the knowledge loop: agentic workflows, peer-reviewed artifact creation, and structured knowledge trees, so that running an analysis also improves the foundation for the next session.
You'll also keep the foundations solid: the pipelines that extract, load, and transform data from source systems into the warehouse, where availability and freshness are prerequisites for everything else, and the access control framework (Snowflake roles, PII/PHI provisioning, least-privilege at scale) that we own with our Security team for compliance requirements. As we increasingly invest into training and hosting production ML models, you’ll support the engineering teams’ needs to build, deploy, and monitor these systems.
Our customers are our stakeholders, and we prioritize getting to the root cause of their problems and delivering systematic solutions. We measure ourselves by the reliability, adoption, quality, speed of the decisions our data enables.
Our data stack: Snowflake as the warehouse, dbt for transformation, Airbyte and Hevo for ingestion, Hex and Metabase for BI, and various agents (Claude, Codex, Snowflake Cortex, Hex AI, etc.) for AI-assisted analysis. Source systems are largely MongoDB and Postgres.
What we are looking for
We care more about how someone thinks through problems than how polished their narrative is. A successful Senior Data Engineer at Clipboard exhibits the following traits:
First-principles thinking: you don't default to past experience. You dig into what's actually going on in a source system, a metric discrepancy, or a slow pipeline before deciding what to do.
Customer-centricity: you stay close to your stakeholders, understand the decisions their data enables, and use that context to prioritize your focus on the right problems.
Ownership and judgement: you're comfortable owning infrastructure other people depend on. A pipeline that runs late or a metric that's slightly wrong breaks decisions downstream, and we want people who treat that responsibility like their own business.
Technical strength: the work spans pipelines, semantic modeling, governance, and AI-facing knowledge infrastructure. You don't need to be an expert in all of it, but you should be interested in the full scope rather than hoping to stay in one lane.
The Interview Process
When looking at candidates, their actual competencies matter more to us than what’s on your resumes. Because of that we make sure our assessments and interviews mirror real work that’s being done at Clipboard.
Here's what the process looks like:
Live, technical interview (SQL), 60 min.
Live, technical interview (architecture design), 60 min.
Hiring Manager Interview, 60 min.
Final culture screen with our Head of People, 30 min.
Offer!
Why Clipboard
Clipboard Health is a Series C, YC-backed company that has been profitable since 2022. We fill millions of shifts annually across the U.S. and are still growing fast. If that's the kind of place you want to build, apply here, or reach out directly. We review every submission.
Quick Note on Scammers:
Clipboard would never ask you for money or your bank details to participate in our hiring process. Report any scammers impersonating the Clipboard hiring team members here.
