Full Stack Data Engineer, Synapticure

• Proven experience driving decision-making as a Data Engineer or similar role
• Experience working with CMS, clinical, or healthcare data
• Strong SQL skills and experience with the modern data stack (e.g., dbt, Snowflake, Looker)
• Proficiency with data visualization tools (e.g., Knowi, Tableau, Power BI, Looker)
• Experience applying machine learning to healthcare use cases such as patient stratification, care pathway optimization, and forecasting
• Familiarity with cloud platforms (e.g., AWS or Google Cloud)
• Ability to turn ambiguous business questions into structured analysis
• Effective communicator comfortable working across teams
• Detail-oriented, proactive, and eager to learn
• Team player with humility, curiosity, and a collaborative spirit

Desirable

• Synapticure is seeking a highly motivated and experienced Full Stack Data Engineer to join our growing data team. Reporting to the Head of Data, you’ll work cross-functionally with engineering, clinical, product, finance, and operations teams to drive data-informed decision-making across the organization
• You will own the full analytics lifecycle — from identifying data requirements and building pipelines, to modeling datasets, uncovering insights, and creating tools that help stakeholders act. Your work will directly influence strategic direction, improve patient outcomes, and unlock efficiencies across the company
• The ideal candidate is relentlessly curious, thrives in ambiguity, and combines strong technical fluency with thoughtful communication. You’ll play a key role in stewarding high-integrity, actionable data and building a culture of data literacy and continuous improvement
• Partner with cross-functional teams to scope and execute high-impact analytical projects
• Build validated, documented, and QA-tested datasets from multiple source systems
• Design and build dashboards and tools for clinical, product, and operational visibility
• Conduct exploratory and statistical analyses to identify opportunities and insights
• Own the full data lifecycle: requirements → pipeline → modeling → visualization → insight
• Maintain high standards of data quality through robust documentation and testing
• Clearly communicate insights and recommendations to both technical and non-technical stakeholders
• Contribute to our internal knowledge base and support data literacy initiatives