Staff Data Engineer
New
9
9amHealthHealth tech
Location: United States; Workplace: RemoteFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 10+ years of professional experience in data engineering or a hybrid data/software engineering role.
- Required Skills
- PythonSQLData modeling
Requirements
- Bring 10+ years of professional experience in data engineering or a hybrid data/software engineering role.
- Write, test, debug, and ship production code independently, using practices such as code review and CI/CD.
- Have expert-level Python experience architecting production data pipelines, ETL logic, and reporting systems.
- Have expert-level SQL skills, including window functions, CTEs, recursive queries, and query optimization at scale.
- Bring hands-on experience with cloud data services, ideally AWS Glue, S3, Redshift, and Athena.
- Have production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning.
- Have experience handling sensitive data such as PHI with appropriate access controls and compliance practices.
- Understand data modeling techniques, including star and snowflake schemas, slowly changing dimensions, and event-based models.
- Demonstrate experience setting technical direction and standards that other engineers and teams build on.
- Have professional experience with AI-assisted development tools and sound judgment on correctness, security, and review.
- Explain technical trade-offs and risks to technical and non-technical stakeholders, including executives.
- Preferred: professional-level cloud certification or equivalent hands-on experience; experience in health tech, digital health, or another regulated industry; and experience with CI/CD for data assets.
- Preferred: background with AI/ML features, infrastructure-as-code, containerized deployments, orchestration tools, BI platforms, or semantic layers.
Responsibilities
- Define and own the data platform architecture, technical standards, and coding practices.
- Evaluate data technologies and contribute to the platform roadmap, including performance and cost optimization.
- Design, build, and maintain scalable Python ETL/ELT pipelines using AWS Glue.
- Drive analytical data models and the semantic layer for self-service BI and advanced analytics.
- Build integrations between the data platform and internal or third-party systems.
- Author, optimize, and maintain SQL and database objects in Redshift.
- Operate and optimize AWS data infrastructure, including Glue, S3, Redshift, and Athena.
- Own data quality, governance, and platform reliability, including monitoring, alerting, and incident response.
- Partner with data science and ML teams on data preparation, feature engineering, and model monitoring.
- Set technical standards through code review, architecture decisions, and hands-on mentorship.
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