Lead Data Engineer

We are building the future of healthcare analytics. Join us to design, build, and scale robust data pipelines that power nationwide analytics and support our machine learning systems. Our goal : pipelines that are reliable, observable, and continuously improving in production.

This is a fully remote role, open to candidates based in Europe or India, with periodic team gatherings in Mountain View, California.

What You’ll Do

• Design, build, and maintain scalable ETL pipelines using Python (Pandas, PySpark) and SQL, orchestrated with Airflow (MWAA).
• Develop and maintain the SAIVA Data Lake / Lakehouse on AWS, ensuring quality, governance, scalability, and accessibility.
• Run and optimize distributed data processing jobs with Spark on AWS EMR and / or EKS.
• Implement batch and streaming ingestion frameworks (APIs, databases, files, event streams).
• Enforce validation and quality checks to ensure reliable analytics and ML readiness.
• Monitor and troubleshoot pipelines with CloudWatch, integrating observability tools like Grafana, Prometheus, or Datadog.
• Automate infrastructure provisioning with Terraform, following AWS best practices.
• Manage SQL Server, PostgreSQL, and Snowflake integrations into the Lakehouse.
• Participate in an on-call rotation to support pipeline health and resolve incidents quickly.
• Write production-grade code, and contribute to design / code reviews and engineering best practices.

What We’re Looking For

Scala or Java a plus.

familiarity with Docker, ECS, and CI / CD pipelines.