Role Overview

We are seeking a Data Engineer to design, build, and operate the data infrastructure powering our next-generation healthcare platform. This role focuses on building reliable ETL and data pipeline systems, enforcing PHI-safe data boundaries, and preparing structured data assets that support downstream AI and LLM workloads.

You will work alongside our DBA to co-design schemas, own ingestion and transformation pipelines, and ensure that all data flows conform to our HIPAA-aligned architecture. You will operate in a modern, AI-assisted engineering organization leveraging Python, Google Cloud Platform, and BigQuery, within a GitHub-based DevSecOps workflow. This is a hands-on, high-impact role for a data engineer who understands regulated healthcare data and is fluent with AI-assisted development tools.

Responsibilities

Design, build, and maintain ETL and ELT pipelines for ingesting, transforming, and delivering clinical and operational data
Enforce PHI data boundaries across all pipeline stages — no PHI in non-production environments, CI/CD pipelines, or logs
Build and manage data flows across GCP services (BigQuery, Cloud SQL, Pub/Sub, Cloud Storage, Dataflow)
Co-design database schemas with the DBA to support clinical data models, FHIR-aligned structures, and AI-ready data formats
Prepare and structure data for LLM consumption — including chunking strategies, metadata tagging, context-window-aware formatting, and embedding-friendly schema design
Implement de-identification and synthetic data generation pipelines for non-production use
Build audit trails and data lineage tracking to support HIPAA compliance and SOC 2 evidence requirements
Integrate EHR and wearable data sources via FHIR R4 APIs and vendor-specific connectors
Ensure adherence to security, privacy, and compliance requirements (HIPAA-aligned data architecture)
Contribute to CI/CD pipelines and infrastructure-as-code practices
Participate in code reviews and enforce data engineering standards
Leverage AI-assisted development tools (e.g., Claude Code, Codex) to accelerate pipeline development and code quality
Troubleshoot and resolve data pipeline failures and data quality issues in production
Build and maintain BI dashboards and data visualizations to support clinical and operational reporting needs

Requirements

5+ years of data engineering experience building and operating production-grade pipelines
Strong proficiency in Python for data engineering workloads (pandas, SQLAlchemy, Apache Beam or equivalent)
Strong SQL skills — schema design, query optimization, and data modeling
Hands-on experience with GCP data services (BigQuery, Cloud SQL, Pub/Sub, Cloud Storage) or equivalent AWS/Azure services with demonstrated ability to ramp on GCP
Experience designing and operating ETL/ELT pipelines at scale
Understanding of data boundary controls and PHI handling requirements in regulated environments
Experience with CI/CD workflows and GitHub-based development
Strong communication and problem-solving skills