Sr. Data Engineer

Clipboard is a healthcare staffing marketplace that connects healthcare professionals with workplaces needing skilled workers. The Senior Data Engineer will build reliable, governed data and knowledge infrastructure, including semantic models, AI-facing workflows, data pipelines, access controls, and support for production machine learning systems.

Responsibilities:

• 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
• 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
• 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

Requirements:

• 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