7+ years of engineering experience focused on data engineering or backend systems with substantial data processing.
Strong proficiency in SQL and TypeScript for building production pipelines.
Experience designing ETL/ELT pipelines and data models with orchestration workflows.
Solid understanding of relational databases, data warehouses, and cloud infrastructure.
Ability to manage production data systems from design to ongoing support.
Good judgment in product and workflow decisions related to data usage.
Practical approach to data quality and handling sensitive information.
Comfortable operating in ambiguous environments.
Responsibilities
Own data initiatives from design to production without constant oversight.
Build and maintain scalable data ingestion and transformation pipelines.
Connect data architecture to customer and internal team workflows.
Design reusable data models for product features and analytics.
Ensure data quality and production readiness through validation and monitoring.
Improve performance and reliability as data volume and complexity grow.
Collaborate with cross-functional teams to translate business needs into data solutions.
Contribute to technical design, code reviews, and mentorship in data engineering practices.
Benefits
Collaborative work environment with strong ownership.
Opportunities for mentorship and professional growth.
Engagement in complex and impactful data initiatives.
Access to cutting-edge tools and technologies in data processing.
Chance to work in a fast-growing startup environment.
Full Job Description
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!
