Lead Data Engineer

About the team /role overview

This Lead Data Engineer will be the engine behind our platform's acceleration. Working alongside our infrastructure and automation lead, this role transforms our ability to build reliable, scalable data pipelines that power everything from executive dashboards to AI predictions. Without this foundation, the insights layer cannot exist. 

Why This Role Matters 

Today, our platform work is bottlenecked by capacity. The team manages some of the infrastructure, automation, and pipeline development — all critical, all competing for the same resource. This position unlocks: 

· Parallel development — build multiple domain data models simultaneously 

· AI readiness — dedicated capacity for feature engineering and ML pipelines 

· Faster delivery — cut time-to-insight from months to weeks 

· Platform stability — proactive monitoring instead of reactive fixes 

Position Summary

The Laed Data Engineer designs, builds, and maintains the data infrastructure that powers Delta's Health Intelligence Platform. This role owns the pipelines that transform raw healthcare data from vendors (UMR, UHC, CVS, Spring Health) into clean, trusted, AI-ready data assets. You'll work with modern tools — dbt, Redshift Serverless, AWS SageMaker — and collaborate with data quality, visualization, and data science teams to deliver enterprise-scale healthcare analytics.

KEY RESPONSIBILITIES:

· Build and optimize dbt models across staging, intermediate, domain, and marts layers following healthcare data best practices 

· Design and implement data pipelines for healthcare vendor data ingestion, transformation, and quality validation 

· Develop automated monitoring and alerting to catch data issues before they impact downstream analytics 

· Create ML feature engineering pipelines that prepare data for predictive models in SageMaker and Bedrock 

· Optimize Redshift performance through query tuning, table design, and workload management 

· Build CI/CD pipelines for reliable, automated deployments across environments 

· Document data models and pipelines to enable team knowledge sharing and platform maintainability