Senior Data Engineer, Ambience Healthcare

• 5+ years in a production Data Engineering role, with hands-on Snowflake experience (data modeling, performance tuning, warehouse administration)
• Strong SQL skills and proficiency in Python; experience building ETL/ELT pipelines, data lakes, or warehouses in modern cloud environments
• Solid grasp of data validation, schema design, and scalable architecture
• A clear communicator who can bridge technical and non-technical teams, gather requirements, and present to cross-functional stakeholders
• Mission-driven, thrives in a fast-paced startup environment, and takes ownership of deliverables
• You'll thrive here if you've owned a production pipeline end-to-end and know why observability matters because you've been paged when data went stale; you move fast and take real ownership of data quality; and you're comfortable making calls without a fully-specified ticket

Desirable

• Ambience runs on data: which clinicians are adopting the product, how much time it saves them, whether documentation quality holds up across specialties. Getting those answers right—and getting them fast, reproducibly, and in a form a customer can verify—is a hard data engineering problem, and it's the one you'll own
• As a Senior Data Engineer, you'll build and run the pipelines that ingest clinical and operational data, model it into governed metrics, and put trusted self-service analytics in front of every team at the company
• You'll work cross-functionally with product managers, engineers, clinicians, and GTM to turn raw data into the numbers the whole company, and our customers, make decisions on. If a metric is wrong, stale, or can't be explained, that's yours to fix—and the credibility of our platform rides on you getting it right
• Own the Trust Layer: Build and run the pipelines that ingest, validate, and transform clinical and operational data at scale—so the metrics powering our AI story are accurate, reproducible, and defensible. If the data breaks, you own the fix
• Enable Insights Across Teams: Partner with engineering, clinical, and product teams to design clear, actionable dashboards and analytics that guide decision-making and improve healthcare outcomes
• Support Scalable Infrastructure: Apply best practices around warehousing, orchestration (Dagster), governance, and RBAC to keep our data systems secure, performant, and ready for rapid innovation as load increases
• Automate Data Ingestion Workflows: Build file-based ingestion pipelines enabling plug-and-play onboarding of external data sources. Develop automated validation, triggering, and error-handling mechanisms for real-time data availability
• Establish Validation & Transformation Pipelines: Implement data validation frameworks using Python or TypeScript, and design transformation layers (SQL, dbt) that standardize and cleanse raw data for analysis and operational workflows
• Deliver Governed, Trusted Analytics: Build and maintain governed metrics and analytical schemas in SQL that stand up to customer and third-party verification, and enable tiered self-service so every employee—technical or not—can answer their own questions without waiting on the data team