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Heidi Health is building an AI Care Partner platform for clinicians, processing 2.8 million patient sessions weekly across 190+ countries in 110 languages. The company has grown ARR from $1M to $50M in two years and recently raised $340M (Series C led by Blackbird plus $240M growth investment from General Catalyst).
As Senior Analytics Engineer, you will partner with analysts, data engineers, and business teams to design, build, and maintain scalable data models powering reliable insights, experimentation, and performance tracking. You'll work alongside Clinical AI, LLMOps, and Applied AI engineers to bring evaluations and LLM-powered data enrichment into production. Key responsibilities include developing and managing data pipelines across regional workspaces on AWS and Azure; using SQL and analytical thinking to transform complex data into clear, usable structures; designing product event tracking with engineers; contributing to customer-facing data products like the Heidi analytical API and data shares with health systems; improving data access for self-serve insights via Omni dashboards and reverse ETL into tools like Braze and HubSpot; championing data quality, testing, documentation, and governance; and contributing to shared practices and frameworks that make analytics more efficient and scalable.
Heidi's culture emphasizes building for the next decade, leading proactively, following evidence, owning outcomes end-to-end, shipping and iterating rapidly, and understanding clinicians' real-world workflows. The company values sustainable performance, mental health, and outcomes over process theater. True A-players progress extremely fast in this scale-up environment.
REQUIREMENTS:
- 3–5 years of experience in analytics engineering, data engineering, or similar data-focused role in fast-paced or high-growth environment
- Strong proficiency in SQL and experience transforming data for analysis, reporting, and product use cases
- Working knowledge of Python and PySpark
- Experience with modern data platforms and workflows, ideally including dbt, Databricks, Airflow, Snowplow, and reverse ETL or BI tools such as Omni
- Solid understanding of data modelling, data quality, and testing best practices
- Ability to work with analysts, engineers, AI teams, and business stakeholders to translate data needs into scalable, reliable solutions
- Interest in AI and LLM workloads (evaluations, model-based enrichment) and appetite to go deeper alongside AI engineers
- Experience supporting or developing customer-facing data products, with care in handling sensitive health data (PHI, PII)
- Strong communication skills with focus on clarity, documentation, and long-term maintainability
- Curiosity, accountability, and drive to improve how data is built, managed, and used across the business
