Principal, Data Engineer

Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first.We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together.

The Principal, Data Engineer is the highest-level individual hands on contributor and technical authority in the data engineering function, responsible for setting platform engineering standards and driving measurable improvements in data quality, pipeline performance, and platform scalability across the Medicare Advantage data ecosystem. This role partners with data architects, AI/ML engineers, analytics leaders, compliance teams, and business stakeholders to translate strategic data priorities into durable, audit-ready platform solutions spanning clinical, operational, and financial domains. The Principal sets the technical bar for the data engineering team, serving as the authoritative voice in architecture decisions, platform investments, and cross-functional engineering forums. Key outcomes include improved pipeline reliability, accelerated delivery of data products supporting risk adjustment, HEDIS, and CMS reporting, and a measurably stronger data engineering team.

Set Platform Engineering Standards: Define andmaintainthe data engineering patterns, pipeline standards, and architectural guardrails that govern how data systems are designed, built, and evolved across the organization's data platform.

Design and Govern the Data Platform: Lead the design of scalable data processing frameworks and transformation models supporting claims, encounters, risk adjustment, and member data domains — with direct accountability for platform reliability and data quality outcomes.

Integrate AI/ML into Production Engineering: Apply machine learning and automation techniques within production data engineering environments —including pipeline intelligence, data quality automation, and feature pipeline design —establishingpatterns others can scale.

Build and Optimize Data Pipelines: Implement and continuously improve high-performance cloud-based data pipelines using Apache Kafka, Databricks, Apache Spark, and cloud-native tools for real-time and batch workflows.

Elevate Engineering Capability Across the Team: Serve as the technical subject matter expert for data engineering, mentoring engineers through architecture reviews, design sessions, and internal documentation — raising the overall capability and consistency of the team.

Lead Technology Evaluation and Platform Roadmap Input: Evaluate emerging data technologies and translate findings into actionable build-versus-buy recommendations, contributing directly to the multi-year data platform engineering roadmap.

Deliver Compliant Healthcare Data Solutions: Applyexpertisein Medicare Advantage data constructs — including CMS requirements, HIPAA, HEDIS, and risk adjustment — to ensure all data solutions are audit-ready, regulatory-compliant, and aligned to health plan reporting standards.

This role carries no direct supervisory responsibility at this time.The Principal, Data Engineer is expected to provide informal technical leadership, mentorship, and subject matter guidance to Data Engineers and Senior Data Engineers as the function's highest-level individual contributor.

Experience

10+ years of progressive data engineering experience, withdemonstratedplatform engineering at scale

5+ years of experience in healthcare, health insurance, or a Medicare Advantage environment

Demonstrated experience building and governingenterprisedata platforms using Apache Kafka, Databricks, and non-relational databases

Proven experience designing complex ETL/ELT pipelines in cloud environments (Azure, AWS, or GCP)

Experience working in HIPAA-compliant data environments with PHI/PII datasets

Demonstratedtrack recordof setting engineering standards or architectural patterns adopted by others

Experience as a technical lead in a data platform modernization or cloud migration initiative

Working on AI tooling and usage in Data Platform space

Hands-on experience with Medicare Advantage–specific data: encounter submissions, RADV, HEDIS, CMS reporting, or Star ratings analytics

Prior work in a high-growth or transformation environment where platform engineering was a strategic priority

Education

Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field; equivalent combination of education and experience considered

Master's degree in a related technical discipline

Training

Demonstrated workingproficiencywith cloud data platform tools (Azure Databricks, Azure Data Factory, or equivalent); hands-on experience accepted in lieu of formal certification

Cloud platform certification (Microsoft Azure Data Engineer Associate, AWS Certified Data Engineer, or Google Professional Data Engineer)

AI/ML engineering certification or relevantDataOpstraining

Data Pipeline & Platform Engineering: Expert-level design, implementation, and optimization of complex data pipelines using Kafka, Databricks, Apache Spark, and cloud-native tooling across real-time and batch architectures.

Cloud Data Platforms: Deep experience with Azure (preferred), AWS, or GCP services including data lakes,lakehouses, and streaming architectures.

AI/ML Integration in Data Engineering: Demonstratedability to operationalize machine learning and automation within production data engineering pipelines, including feature stores and model serving infrastructure.

Healthcare Data Standards & Compliance: Advanced knowledge of HIPAA, HL7/FHIR, ICD-10/CPT codes, CMS data submission standards, and PHI/PII data governance requirements.

Data Modeling: Ability to design data models for relational and non-relational systems that scale to support healthcare analytics use cases across clinical, operational, and financial domains.

SQL & Programming Languages: Advanced SQLproficiencywith production-level experience in Python, Scala, or Java for data engineering workloads.

Data Quality & Governance: Experience implementing data quality frameworks, lineage tracking, and metadata management within large-scale data platforms.

Licensure

Required: None