Design, build, and optimize scalable data pipelines supporting enterprise healthcare analytics and reporting use cases
Lead automation efforts across ingestion, transformation, orchestration, and operational monitoring workflows
Develop and maintain ingestion frameworks for structured and semi-structured healthcare data sources
Build and expand semantic data layers within GCP and BigQuery to support consistent, trusted business reporting and analytics consumption
Design scalable ELT/ETL workflows leveraging modern cloud-native architecture patterns
Partner closely with architects, analytics teams, and business stakeholders to translate data requirements into scalable technical solutions
Optimize BigQuery performance, partitioning, and cost management strategies
Implement engineering best practices for testing, observability, reliability, and deployment automation
Support data governance, data quality, and metadata management initiatives across the platform
Mentor junior engineers and provide technical leadership across delivery teams

7+ years of experience in data engineering or cloud data platform development
Strong expertise in Google Cloud Platform (GCP), particularly: BigQuery, Cloud Storage, Cloud Composer, Dataflow
Experience designing and building scalable data ingestion and pipeline automation frameworks
Strong understanding of semantic layer development and dimensional modeling concepts
Advanced SQL and Python development skills
Experience with ETL/ELT pipeline design and orchestration
Familiarity with CI/CD, infrastructure automation, and cloud engineering best practices
Strong understanding of data quality, observability, and operational reliability principles
Experience working with healthcare data ecosystems and regulated enterprise environments (preferred)
Familiarity with healthcare data standards or payer/provider analytics platforms (preferred)
Exposure to dbt, Looker, or semantic modeling frameworks is a plus (preferred)

Lead Data Engineer

Key skills