Staff Data Engineer

Join our team as a Staff Data Engineer, where you'll play a crucial role in shaping the future of our data platform. You'll be responsible for defining and owning the architecture, technical standards, and coding practices for the data platform, as well as designing, building, and maintaining scalable ETL/ELT pipelines. You'll also drive the analytical data models and semantic layer that power self-service BI and advanced analytics. This is a hands-on role that requires expert-level Python and SQL experience, as well as a solid understanding of data modeling techniques and cloud data services.

• Définir et posséder l'architecture, les normes techniques et les pratiques de codage pour la plateforme de données.
• Concevoir, construire et maintenir des pipelines ETL/ELT évolutifs en Python en utilisant AWS Glue.
• Évaluer et recommander des technologies de données, et contribuer à la feuille de route de la plateforme, y compris l'optimisation des performances et des coûts.

• Expert-level Python experience architecting production data pipelines, ETL logic, and reporting systems. Python is the primary language, and depth in other languages relevant to modern data engineering (such as Java, Scala, or Go) and sound tooling decisions are valued
• Ability to explain technical trade-offs and risks clearly to technical and non-technical stakeholders, including executives
• Professional use of AI-assisted development tools, with sound judgment on correctness, security, and review
• Demonstrated experience setting technical direction and standards that other engineers and teams build on
• Expert-level SQL, including window functions, CTEs, recursive queries, and query optimization at scale
• A solid understanding of data modeling techniques, including star and snowflake schemas, slowly changing dimensions, and event-based models
• Production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning
• A strong data or software engineering background, with the ability to write, test, debug, and ship production code independently using practices such as code review and CI/CD
• Hands-on experience with cloud data services, ideally AWS (such as Glue, S3, Redshift, or Athena), including building and operating production data pipelines on them
• 10+ years of professional experience in data engineering or a hybrid data/software engineering role
• Experience handling sensitive data, such as PHI, with appropriate access controls and compliance practices
• Experience with CI/CD for data assets (such as dbt CI, Great Expectations, or similar)
• A track record of improving developer experience and productivity through tooling, automation, or process improvements, and of influencing engineering practice beyond your immediate team
• Professional-level cloud certification (such as AWS Solutions Architect – Professional, Google Cloud Professional Data Engineer, or Azure Solutions Architect Expert), or equivalent hands-on experience
• A background building or contributing to AI/ML features, such as feature stores, training pipelines, model serving, or RAG architectures
• Prior experience in a startup or high-growth environment, owning outcomes end to end
• Experience in health tech, digital health, or another regulated industry
• Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent
• Comfort with infrastructure-as-code (such as Terraform or CloudFormation) and containerized deployments (such as Docker, ECS, or EKS)
• Familiarity with BI platforms and semantic layers, such as Looker/LookML, in support of self-service analytics