Principal Data Engineer – ML Platforms

Overview

Altarum | Data & AI Center of Excellence (CoE)
Altarum is building the future of data and AI infrastructure for public health. We are hiring a Principal Data Engineer – ML Platforms to design, build, and operationalize modern data and ML platform capabilities that power analytics, evaluation, AI modeling, and interoperability across all Altarum divisions.

What You'll Work On

• ML Platform Engineering: lakehouse architecture, pipelines, MLOps lifecycle
• Applied ML Enablement: risk scoring, forecasting, Medicaid analytics
• NLP/Generative AI Support: RAG, vectorization, health communications
• Causal ML Operationalization: evaluation modeling workflows
• Responsible/Trusted AI Engineering: model cards, fairness, compliance

Responsibilities

• Platform Architecture & Delivery: design and operate modern, cloud-agnostic lakehouse using object storage, SQL/ELT engines, and dbt.
• Build CI/CD pipelines for data, dbt, and model delivery (Git Hub Actions, Git Lab, Azure Dev Ops).
• Implement MLOps systems: MLflow or equivalent, feature stores, model registry, drift detection, automated testing.
• Engineer solutions in AWS Gov Cloud today, with portability to Azure Gov or GCP.
• Use IaC (Terraform, Cloud Formation, Bicep) to automate secure deployments.

Pipelines & Interoperability

• Build scalable ingestion and normalization pipelines for healthcare and public health datasets, including FHIR R4 / US Core (preferred), HL7 v2 (preferred), Medicaid/Medicare claims & encounters (preferred), SDOH & geospatial data (preferred), survey 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 & Generative AI

• 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.
• Build ingestion and vectorization pipelines for surveys, interviews, and unstructured text.
• Support RAG systems for synthesis, evaluation, and public health guidance.
• Enable 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

• 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 Gov Cloud 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…