Design, build, and maintain scalable batch and streaming pipelines using Google Cloud Dataflow, Google Cloud Datastream, Airbyte, and orchestration tools (Airflow/Prefect/Dagster).

Develop and optimize ETL/ELT processes across AWS Postgres, Google FHIR Store, and Google BigQuery.

Build and maintain unified data models that integrate multiple healthcare data sources (EHR/FHIR, claims/X12, ADT/HL7, CRM, transactional Postgres, Tuva, and third-party APIs).

Implement dbt/OBT transformations to create curated semantic layers for AI/LLM, BI, and predictive analytics.

Ensure data quality, lineage, validation, and governance while maintaining HIPAA compliance and PHI/PII security.

Collaborate with AI/ML engineers, BI developers, and product teams to enable data-driven features, dashboards, and predictive models.

Implement monitoring, anomaly detection, and pipeline optimization for performance, reliability, and cost efficiency.

Participate in architecture discussions, code reviews, and mentoring, setting best practices for data engineering.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
  
• 5+ years of hands-on experience in data engineering (cloud-native environments preferred).
  
• Strong proficiency in SQL and Python for data engineering workflows.
  
• Proven experience with:
  
• Google Cloud Dataflow, Datastream, or Airbyte (streaming & ingestion pipelines)
  
• AWS Postgres (transactional & analytical use cases)
  
• Google FHIR Store (FHIR APIs & EHR ingestion)
  
• Google BigQuery (large-scale data warehouse/lakehouse)
  
• dbt/OBT for transformations and modeling
  
• Experience designing and maintaining unified data models from heterogeneous healthcare data sources.
  
• Familiarity with healthcare data standards: HL7, FHIR, X12 EDI (837/835), ICD, SNOMED, ADT, CCD/CCDA.
  
• Experience ensuring HIPAA-compliant data handling, governance, and observability.
  
• Knowledge of data modeling (star/snowflake schemas) and cloud-native architectures (AWS/GCP).
  

Good to Have

• Experience with Health care standardization frameworks.
  
• Building feature stores for AI/ML pipelines.
  
• Familiarity with real-time streaming.
  
• Hands-on with Power BI or other BI tools for analytics enablement.
  
• Prior work in Value-Based Care, ACOs, MSOs, or population health environments.
  
• Ability to mentor junior engineers and establish team-wide best practices.
  
  
  
  

Benefits

• Build impactful data pipelines powering AI, LLMs, and BI in healthcare.
  
• Work on meaningful problems that directly improve patient outcomes and provider efficiency.
  
• Be part of a growing tech-first healthcare company with strong domain expertise.
  
• Collaborative culture with AI, data, product, and engineering teams.
  
• Competitive compensation, benefits, and career growth opportunities.