Principal Data Engineer – ML Platforms

Altarum | Data & AI Center of Excellence (CoE)
Altarum is building the future of data and AI infrastructure for public health - and we’re looking for a Principal Data Engineer – ML Platforms to help lead the way. In this cornerstone role, you will design, build, and operationalize the modern data and ML platform capabilities that power analytics, evaluation, AI modeling, and interoperability across all Altarum divisions.
If you want to architect impactful systems, enable data science at scale, and help ensure public health and Medicaid programs operate with secure, explainable, and trustworthy AI - this role is for you. What You'll Work On
This role blends deep engineering with applied ML enablement: ML Platform Engineering: modern lakehouse architecture, pipelines, MLOps lifecycle Applied ML enablement: risk scoring, forecasting, Medicaid analytics NLP/Generative AI support: RAG, vectorization, health communicationsCausal ML operationalization: evaluation modeling workflowsResponsible/Trusted AI engineering: model cards, fairness, compliance  Your work ensures that Altarum’s public health and Medicaid programs run on secure, scalable, reusable, and explainable data and AI infrastructure. 

What You'll Do

• Platform Architecture & Delivery  
• 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. 

Pipelines & Interoperability  
• Build scalable ingestion and normalization pipelines for healthcare and public health datasets, including: 
• FHIR R4 / US Core (strongly preferred) 
• HL7 v2 (strongly preferred) 
• Medicaid/Medicare claims & encounters (strongly preferred) 
• SDOH & geospatial data (preferred) 
• Survey, mixed-methods, and qualitative data 
• 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. 

Data Quality, Reliability & Cost Management 
• 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. 

Applied ML Enablement 
• 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. 
• Integrate ML assets into standardized deployment workflows. 

Generative AI Enablement 
• Build ingestion and vectorization pipelines for surveys, interviews, and unstructured text. 
• Support RAG systems for synthesis, evaluation, and public health guidance. 
• Enable Palladian Partners with secure, controlled-generation environments. 

Causal ML & Evaluation Engineering  
• Translate R/Stata/SAS evaluation code into reusable pipelines. 
• Build templates for causal inference workflows (DID, AIPW, CEM, synthetic controls). 
• Support operationalization of ARA’s applied research methods at scale. 

Responsible AI, Security & Compliance  
• 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. 
• Enforce privacy-by-design: tokenization, encryption, least-privilege IAM, and VPC isolation. 

Reuse, Shared-Services, and Enablement 
• Develop runbooks, architecture diagrams, repo templates, and accelerator code. 
• Pair with data scientists, analysts, and SMEs to build organizational capability. 
• Provide technical guidance for proposals and client engagements. 

Your First 90 Days - You will make a meaningful impact fast. Expected outcomes include:  
• Platform skeleton operational: repo templates, CI/CD, dbt project, MLflow registry, tests. 
• Two pipelines in production (e.g., FHIR → analytics and claims normalization). 
• One end-to-end Co