• 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.