Optum is hiring a Senior Data Engineer to help build and run scalable, governed data architectures and pipelines supporting healthcare data solutions. This remote role (based in Eden Prairie, MN) focuses on production-grade ETL/ELT, cloud data platforms on Azure and Databricks, and the operational rigor needed to deliver trusted data for analytics and AI/ML stakeholders.

In this position, you will lead end-to-end delivery of data engineering solutions, from ingestion and transformation through data quality, governance, security for PHI/PII, and continuous improvement of production operations.

Responsibilities

Lead the design and delivery of scalable, governed data engineering solutions, including technical direction, implementation planning, risk management, and production readiness for data products used by business, analytics, and AI/ML teams
Develop and manage robust ETL/ELT pipelines to ingest, transform, and integrate data from heterogeneous sources such as relational databases, APIs, file shares, and end-user-computing assets
Create Python and PySpark solutions for distributed processing, automation, integration, and reusable transformation logic
Optimize data platform performance and cloud consumption using SQL tuning, Spark job optimization, storage format selection, partitioning strategies, cluster configuration, and workload scheduling
Build and optimize workflows on Azure Cloud services and Databricks, including medallion (bronze/silver/gold) layering and orchestrated pipelines
Migrate business logic from legacy and end-user-computing systems into documented, testable transformation logic over governed tables
Define data quality practices such as validation, reconciliation, and parity testing to ensure accuracy, reliability, auditability, and consistency with replaced systems
Establish data lineage, ownership, and cataloguing, including documented rollup and allocation rules when source systems operate at differing grains
Implement security and compliance controls for handling PHI/PII and confidential business data in line with HIPAA or similar regulations, including de-identification and masking for controlled sharing
Build and improve CI/CD, version control, and automated testing practices for data pipelines
Implement monitoring, alerting, logging, and operational runbooks to support pipeline reliability, timely issue detection, incident response, and continuous improvement
Enable AI/ML and advanced analytics workflows by provisioning trusted data, scalable infrastructure, and governed consumption patterns
Design and publish reusable data products, data services, APIs, and consumption layers so downstream teams consume trusted data consistently
Partner with product owners, business stakeholders, architects, data scientists, analysts, and engineering teams to translate requirements into scalable solutions and communicate technical tradeoffs clearly
Design, develop, and deploy AI-enabled data solutions supporting responsible AI use and leveraging AI-powered development tools to improve productivity and delivery quality
Mentor data engineers and analysts through design reviews, code reviews, and best-practice guidance to improve team delivery quality

Requirements

6+ years of overall experience in data engineering, data warehousing, distributed data processing, or enterprise data platform delivery
6+ years of experience with SQL, data modeling, and performance tuning for large-scale systems
3+ years of experience with ETL/ELT frameworks, pipeline orchestration, and heterogeneous source-system integration
3+ years of experience with Python for data processing, automation, and integration
3+ years of experience with Databricks, Spark/PySpark, and Delta Lake
3+ years of experience with public cloud platforms such as Azure or GCP
1+ year of experience integrating data from heterogeneous source systems
1+ year of experience with data quality, validation, reconciliation, observability, and production support practices for large-scale data pipelines
1+ year of experience with Git, CI/CD platforms, automated testing frameworks, release management, and deployment practices for production data pipelines
1+ year of experience in AI-powered development tools to improve engineering productivity and delivery quality

Technologies

Python, PySpark, SQL, Databricks, Spark, Delta Lake
Azure, GCP, ETL, ELT
Git, CI/CD
HIPAA
Azure Data Factory, Airflow, dbt, Unity Catalog, Microsoft Purview
APIs

Benefits

Comprehensive benefits package
Incentive and recognition programs
Equity stock purchase
401k contribution

Flexibility and Remote Work

Remote work is available from anywhere within the U.S.
For hires in the Minneapolis or Washington, D.C. area, required in-office presence is a minimum of four days per week

Preferred Qualifications

Experience with batch and near-real-time data processing patterns, including event-driven ingestion, streaming pipelines, or comparable technologies
Experience with Azure Data Factory or comparable orchestration tooling such as Airflow, dbt, or similar
Experience migrating legacy or end-user-computing (spreadsheet- or macro-based) business logic into governed data pipelines
Experience with data governance, lineage, and catalog tooling (Unity Catalog, Microsoft Purview, or similar)
Familiarity with AI/ML frameworks and supporting infrastructure for data science workflows
Experience working with PHI/PII and restricted client data in compliance with HIPAA or similar regulations, including de-identification and masking
Healthcare payer, actuarial, pricing, or financial-modelling domain experience

Compensation: Salary is $91,700 - $163,700 per year, based on full-time employment and several factors.

Application deadline: The posting will be live for a minimum of 2 business days or until a sufficient candidate pool is collected. The job posting may come down early due to volume of applicants.

Drug-free workplace: UnitedHealth Group is a drug-free workplace, and candidates are required to pass a drug test before beginning employment.

Senior Data Engineer