Staff Data Engineer

Description

The company runs an AI-enabled virtual specialty care platform for managing high-cost chronic conditions at scale. It works with employers, health plans and pharmacy benefit managers, and its members get care for obesity, diabetes, hypertension and dyslipidemia. Care includes specialist clinicians such as endocrinologists, obesity medicine specialists and clinical pharmacists, at-home lab testing, prescription medications and lifestyle support.

The company was founded in 2021 and is backed by healthcare investors including Define Ventures, SemperVirens, 7Wire Ventures and The Cigna Group Ventures.

The role is a Staff Data Engineer on the data team, reporting to the VP of Data. You will own the data platform that serves current business needs and help decide its direction.

The warehouse, ETL and reporting stack is entirely Python on AWS, so strong Python skills are essential. You will write production Python and SQL, set the architecture and standards the team follows, and work hands-on with AWS services such as Lambda and Glue and with BI tools when needed.

Responsibilities

• Define and own the architecture, technical standards and coding practices for the data platform.
• Assess data technologies, recommend choices, and shape the platform roadmap, including performance and cost.
• Build and maintain scalable ETL and ELT pipelines in Python on AWS Glue.
• Develop the analytical data models and semantic layer behind self-service BI and advanced analytics.
• Build integrations between the platform and internal or third-party systems, writing backend code where needed.
• Set the bar for AI-assisted development across the data team, using tools such as GitHub Copilot, Claude or Cursor.
• Write, tune and maintain complex SQL and database objects such as views and stored procedures in Redshift.
• Run and optimise AWS data infrastructure such as Glue, S3, Redshift and Athena.
• Own data quality, governance and reliability, including monitoring, alerting and incident response.
• Partner with data science and ML teams on data preparation, feature engineering and model monitoring.
• Raise the team's standards through code review, architecture decisions and hands-on mentoring of a data analyst and a data engineer.
• Work with clinical operations, product, finance and engineering to turn unclear business questions into reliable, documented data products.

Requirements

• At least 10 years of professional data engineering or hybrid data and software engineering experience.
• Able to write, test, debug and ship production code on your own, using code review and CI/CD.
• Expert Python, with experience designing production data pipelines, ETL logic and reporting systems. Experience in other languages used in data engineering, such as Java, Scala or Go, is valued, along with sound judgment on tooling.
• Expert SQL, including window functions, CTEs, recursive queries and query optimisation at scale.
• Hands-on work with cloud data services, ideally AWS Glue, S3, Redshift and Athena, building and running production pipelines.
• Production experience with a cloud warehouse, ideally Amazon Redshift, including schema design and performance tuning.
• Experience handling sensitive data such as PHI, with proper access controls and compliance practices.
• Solid grasp of data modelling, including star and snowflake schemas, slowly changing dimensions and event-based models.
• A record of setting technical direction and standards that other engineers and teams adopt.
• Skilled, judicious use of AI-assisted development tools, with attention to correctness, security and review.
• Able to explain technical trade-offs and risks clearly to technical and non-technical stakeholders, including executives.
• Preferred: a professional cloud certification such as AWS Solutions Architect Professional, Google Cloud Professional Data Engineer or Azure Solutions Architect Expert, or equivalent hands-on experience.
• Preferred: experience in health tech, digital health or another regulated industry.
• Preferred: experience with CI/CD for data assets, such as dbt CI, Great Expectations or similar.
• Preferred: work on AI/ML features, such as feature stores, training pipelines, model serving or RAG architectures.
• Preferred: comfort with infrastructure as code (Terraform, CloudFormation) and containerised deployments (Docker, ECS or EKS).
• Preferred: startup or high-growth experience, owning outcomes end to end.
• Preferred: a record of improving developer experience and productivity through tooling, automation or process, and influencing engineering practice beyond your own team.
• Preferred: experience with orchestration tools such as Apache Spark, Airflow or equivalent.
• Preferred: familiarity with BI platforms and semantic layers such as Looker and LookML for self-service analytics.

Conditions

Full-time, W2 positions only.

Team members are distributed across the globe, with physical offices in San Diego, California and Vienna, Austria.

The company values your culture wherever you work.

Equal-opportunity statement: applications are welcome from people of color, LGBTQ+ people, people with disabilities, ethnic minorities and foreign-born residents.

Benefits

• Health, dental and vision insurance.
• Flexible PTO and work-from-home options.
• A professional development budget and support for continuing education.
• Opportunity to contribute to chronic disease management and patient outcomes.