Staff Software Engineer, Ambience Healthcare

• 7+ years in a production Data Engineering role and 2+ years in Software Engineering (or equivalent)
• Strong command of SQL for performance tuning, transformations, and data modeling
• Proficient in scripting languages like Python or TypeScript
• Comfortable building and maintaining data pipelines (ETL/ELT), data lakes, or warehouses in modern cloud environments
• Strong understanding of data validation, schema design, and scalable architectures
• Collaborative Communicator
• Bridges technical and non-technical teams with ease
• Leads discussions, gathers requirements, and simplifies complex data concepts for diverse stakeholders
• Comfortable drafting design docs and presenting to cross-functional teams
• Mission-Aligned
• Passionate about mission-driven work and excited by the opportunity to impact healthcare outcomes with data
• Thrives in a fast-paced, early-stage environment; takes extreme ownership of deliverables

Desirable

• As a Data Engineering Lead at Ambience, you’ll play a pivotal role in building the Data Platform from the ground up to power cutting-edge healthcare AI
• Your work will ensure seamless data ingestion, high data quality, and actionable insights across the company
• This is a high-impact, cross-functional role that partners closely with product managers, engineers, and business stakeholders to architect robust pipelines and deliver analytics solutions that power data-informed decisions
• From automating data ingestion to delivering insights to clinicians and product teams, you’ll architect systems that fuel everything we do with data
• Power AI with Great Data: Build scalable pipelines to ingest, validate, and transform data—laying the foundation for reliable AI systems and model development
• 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
• Architect Scalable Infrastructure: Define best practices around warehousing, indexing, and governance to ensure our data systems are secure, maintainable, and ready for rapid innovation
• 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 Actionable Analytics: Maintain analytical schemas and materialized views in SQL. Build dashboards that make data accessible and insightful for technical and non-technical users alike