Data Engineer

• Design and operate modern, cloud-agnostic lakehouse architecture using object storage, SQL/ELT engines, and dbt
• Build CI/CD pipelines for data, dbt, and model delivery (GitHub Actions, GitLab, Azure DevOps)
• Implement MLOps systems: MLflow (or equivalent), feature stores, model registry, drift detection, automated testing
• Engineer solutions in AWS and AWS GovCloud today, with portability to Azure Gov or GCP
• Use Infrastructure-as-Code (Terraform, CloudFormation, Bicep) to automate secure deployments
• Build scalable ingestion and normalization pipelines for healthcare and public health datasets
• Create reusable connectors, dbt packages, and data contracts for cross-division use
• Publish clean, conformed, metrics-ready tables for Analytics Engineering and BI teams
• Support Population Health in turning evaluation and statistical models into pipelines
• Define SLOs and alerting; instrument lineage & metadata; ensure ≥95% of data tests pass
• Perform performance and cost tuning (partitioning, storage tiers, autoscaling) with guardrails and dashboards
• Build production-grade pipelines for risk prediction, forecasting, cost/utilization models, and burden estimation
• Develop ML-ready feature engineering workflows and support time-series/outbreak detection models
• Translate R/Stata/SAS evaluation code into reusable pipelines
• Implement Model Card Protocol (MCP) and fairness/explainability tooling (SHAP, LIME)
• Ensure compliance with HIPAA, 42 CFR Part 2, IRB/DUA constraints, and NIST AI RMF standards
• Develop runbooks, architecture diagrams, repo templates, and accelerator code
• Provide technical guidance for proposals and client engagements.

Principal Data Engineer – ML Platforms

Data Engineer

Senior Data Architect

Senior Data Engineer – Financial Transactions, Automation

Staff Data Engineer