We're looking for a
Staff Data Engineer who thrives in an environment with strong ownership and can drive complex data initiatives from concept to production. You'll build the pipelines, data models, and infrastructure that power our product, analytics, and AI workflows; turning complex healthcare data into reliable, usable foundations for our teams and customers.
What You'll Do- Own data initiatives end-to-end: Take a data problem through technical design, implementation, validation, production rollout, and ongoing operation, without requiring constant senior oversight.
- Build reliable data pipelines: Design and maintain scalable ingestion and transformation pipelines across application databases, APIs, third-party integrations, and healthcare data sources.
- Bring systems-to-workflow thinking: Connect data architecture, models, and integrations to how customers, operators, and internal teams use the resulting information.
- Design reusable data models: Create well-structured datasets and shared definitions that support product features, reporting, analytics, and machine learning.
- Own data quality and production readiness: Build validation, monitoring, alerting, and recovery into pipelines so teams can trust the accuracy, freshness, and completeness of their data.
- Scale performance and reliability: Improve processing efficiency, query performance, and infrastructure costs as data volume and product complexity grow.
- Collaborate across teams: Partner with product, operations, platform, ML, and other engineers to translate business needs into practical data solutions.
- Raise the engineering bar: Contribute to technical design, code reviews, documentation, and mentorship while establishing maintainable data engineering practices.
What You'll Bring- 7+ years of engineering experience with a focus on data engineering, data platforms, or backend systems involving substantial data processing.
- Strong proficiency with SQL and TypeScript, including building production pipelines and working with complex datasets.
- Experience designing ETL/ELT pipelines, data models, and orchestration workflows, with an understanding of dependencies, retries, backfills, and schema evolution.
- Strong fundamentals in relational databases, data warehouses, and cloud infrastructure, including query optimization and scalable storage and processing.
- Ability to own production data systems end-to-end, from design through rollout and ongoing support.
- Good product and workflow judgment. You understand how data will be used and make technical decisions that serve those needs.
- A practical approach to data quality, observability, access controls, and handling sensitive information.
- Comfort operating in ambiguity. You can scope, plan, and execute independently on complex, open-ended problems.
- Clear communication with technical and nontechnical partners, including the ability to explain data assumptions, tradeoffs, and limitations.
Nice to Have- Experience working with healthcare data, EHR integrations, or interoperability standards such FHIR.
- Experience supporting ML pipelines, AI products, or datasets used for model training and evaluation.
- Familiarity with tools such as Databricks, dbt, Airflow, Dagster, Spark, or comparable data processing and orchestration frameworks.
- Experience building data infrastructure in a fast-growing startup.
If this sounds like you, we'd love to have a chat!
