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Lead Data Engineer
Remote Data Engineering role with clear candidate location fit.
PostedJul 4, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Lead Data Engineer
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Architecture & Strategy
- Lead design and evolution of our cloud-native data platform built primarily on Google Cloud Platform, including BigQuery, Cloud Storage, Pub/Sub, Cloud Run, Airflow (Cloud Composer), and Healthcare API.
- Inform strategic decisions around multi-cloud or AWS interoperability when needed.
- Establish data engineering best practices, coding standards, and architectural patterns.
Pipeline Development
- Build scalable ETL/ELT pipelines using dbt for transformations and Airflow for orchestration.
- Develop ingestion pipelines for clinical and administrative data in HL7, FHIR, DICOM, and custom formats.
- Develop ingestion and transformation pipelines to be used for AI/ML development and model training.
- Implement streaming and batch dataflows using Pub/Sub, Dataflow, and serverless compute.
- Support or guide integrations with AWS-based partner systems or AWS-hosted data sources when applicable.
Data Modeling & Warehousing
- Design and maintain BigQuery datasets, semantic layers, and warehouse structures.
- Leverage industry standards such as FHIR resources for canonical healthcare models.
- Provide guidance on data modeling and warehouse best practices across both GCP and AWS ecosystems.
Data Quality, Observability & Governance
- Implement data quality frameworks, automated testing, and monitoring.
- Ensure HIPAA compliance and proper handling of PHI/PII across all pipelines and cloud environments.
- Drive lineage, documentation, metadata governance, and dbt docs adoption.
Leadership & Collaboration
- Partner with analytics, product, clinical informatics, and security teams to deliver high-quality, trustworthy data products.
- Provide oversight and technical direction for multi-cloud data integrations with AWS-based systems or partners.
- Assist in the recruitment and development of junior data engineers
Requirements
- 7+ years of data engineering experience; 2–3+ years in a lead or senior technical role.
Requirements
- Deep, hands-on expertise in GCP, particularly:
- BigQuery
- GCP Healthcare API (FHIR and DICOM stores)
- Cloud Storage, Pub/Sub, Cloud Run/Functions
- Strong proficiency with:
- dbt (Core or Cloud)
- Airflow (Cloud Composer or self-managed)
- Python and advanced SQL (BigQuery preferred)
- Hands-on experience with healthcare standards:
- FHIR (R4/US Core), HL7 v2/v3, DICOM, C-CDA, X12
- Strong understanding of PHI handling, HIPAA compliance, and healthcare interoperability.
Details
- BigQuery
- GCP Healthcare API (FHIR and DICOM stores)
- Cloud Storage, Pub/Sub, Cloud Run/Functions
- dbt (Core or Cloud)
- Airflow (Cloud Composer or self-managed)
- Python and advanced SQL (BigQuery preferred)
- FHIR (R4/US Core), HL7 v2/v3, DICOM, C-CDA, X12
- Redshift, Lambda, S3, Glue, Kinesis, Athena, API Gateway, Step Functions
Preferred
- AWS experience, especially with:
- Redshift, Lambda, S3, Glue, Kinesis, Athena, API Gateway, Step Functions
- Experience building or maintaining multi-cloud pipelines bridging GCP and AWS.
- Background with Dataflow/Beam or other stream processing frameworks.
- Experience working with EHR integrations, claims processing, HIEs, or clinical data networks.
- Familiarity with ML-enabled data pipelines or feature engineering in healthcare contexts.
Benefits:
- Competitive salary and benefits package.
- Flexible working arrangements (remote or hybrid options available).
- The opportunity to work on life-changing AI technology that directly impacts patient outcomes.
- Join a team that combines cutting-edge innovation with a mission to save lives and improve health equity.
- Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare.
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