Principal Data Engineer – ML Platforms

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 CoE lighthouse MVP delivered (ingestion → model → metrics → BI). 
• Completed playbooks for GovCloud deployment, identity/secrets, rollback, and cost control. 


Success Metrics (KPIs)  
• Pipeline reliability meeting SLA/SLO targets. 
• ≥95% data tests passing across pipelines. 
• MVP dataset onboarding ≤ 4 weeks. 
• Reuse of platform assets across ≥3 divisions. 
• Cost optimization and budget adherence. 

What You'll Bring

• 7–10+ years in data engineering, ML platform engineering, or cloud data architecture. 
• Expert in Python, SQL, dbt, and orchestration tools (Airflow, Glue, Step Functions). 
• Deep experience with AWS + AWS GovCloud. 
• CI/CD and IaC experience (Terraform, CloudFormation). 
• Familiarity with MLOps tools (MLflow, Sagemaker, Azure ML, Vertex AI). 
• Ability to operate in regulated environments (HIPAA, 42 CFR Part 2, IRB). 


Preferred: 
• Experience with FHIR, HL7, Medicaid/Medicare claims, and/or SDOH datasets. 
• Databricks, Snowflake, Redshift, Synapse. 
• Event streaming (Kafka, Kinesis, Event Hubs). 
• Feature store experience. 
• Observability tooling (Grafana, Prometheus, OpenTelemetry). 
• Experience optimizing BI datasets for Power BI. 

Logistical Requirements

• At this time, we will only accept candidates who are presently eligible to work in the United States and will not require sponsorship.
• Our organization requires that all work, for the duration of your employment, must be completed in the continental U.S. unless required by contract.
• If you’re near one of our offices (Arlington, VA; Silver Spring, MD; or Novi, MI), you’ll join us in person one day every other month (6 times per year) for a fun, purpose-driven Collaboration Day. These days are filled with creative energy, meaningful connection, and team brainstorming!
• Must be able to work during Eastern Time unless approved by your manager.
• Employees working remotely must have a dedicated, ergonomically appropriate workspace free from distractions with a mobile device that allows for productive and efficient conduct of business.