Senior Data Engineer - Remote

UnitedHealth Group•Eden Prairie, MN

13h•$91,700 - $163,700•Remote

About The Position

Optum Tech is seeking a Senior Data Engineer to build, optimize, and maintain scalable data architectures and pipelines that power healthcare data solutions. The role covers data architecture, engineering, modeling, integration, optimization, security, and development of cloud-based data solutions. It delivers reliable, high-performing data platforms for business operations, analytics, and applications while ensuring data quality, governance, traceability, and compliance. The engineer automates validation and monitoring, identifies process improvements, collaborates across business and technology teams, provides technical guidance, and establishes engineering best practices. The position is remote within the U.S. However, hires in the Minneapolis or Washington, D.C. area must work in the office at least four days per week. Remote employees must comply with UnitedHealth Group’s Telecommuter Policy. The role supports data products used by business, analytics, and AI/ML stakeholders and includes production delivery and operational support. The stated annual salary range is $91,700–$163,700, based on full-time employment.

Requirements

At least 6 years of overall experience in data engineering, data warehousing, distributed data processing, or enterprise data platform delivery.
At least 6 years of experience with SQL, data modeling, and performance tuning for large-scale systems.
At least 3 years of experience with ETL/ELT frameworks, pipeline orchestration, and heterogeneous source-system integration.
At least 3 years of experience with Python for data processing, automation, and integration.
At least 3 years of experience with Databricks, Spark/PySpark, and Delta Lake.
At least 3 years of experience with public cloud platforms such as Azure or GCP.
At least 1 year of experience integrating data from heterogeneous source systems.
At least 1 year of experience with data quality, validation, reconciliation, observability, and production support practices for large-scale data pipelines.
At least 1 year of experience with Git, CI/CD platforms, automated testing frameworks, release management, and deployment practices for production data pipelines.
At least 1 year of experience responsibly using AI-powered development tools to improve engineering productivity and delivery quality.
Remote employees must adhere to UnitedHealth Group’s Telecommuter Policy.
Candidates must pass a drug test before beginning employment.

Nice To Haves

Experience with batch and near-real-time processing patterns, including event-driven ingestion or streaming pipelines.
Experience with Azure Data Factory or comparable orchestration tools such as Airflow or dbt.
Experience migrating legacy or spreadsheet- or macro-based business logic into governed data pipelines.
Experience with data governance, lineage, and catalog tools such as Unity Catalog or Microsoft Purview.
Familiarity with AI/ML frameworks and supporting infrastructure for data science workflows.
Experience handling 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.

Responsibilities

Lead the design and delivery of scalable, governed data engineering solutions, including technical direction, implementation planning, risk management, and production readiness.
Develop and manage ETL/ELT pipelines that ingest, transform, and integrate data from heterogeneous sources, including relational databases, APIs, file shares, and end-user-computing assets.
Develop Python and PySpark solutions for distributed data processing, automation, integration, and reusable transformation logic.
Optimize data platform performance and cloud consumption through SQL tuning, Spark job optimization, storage format selection, partitioning, cluster configuration, and workload scheduling.
Build and optimize Azure Cloud and Databricks workflows, including medallion-layered and orchestrated pipelines.
Migrate business logic from legacy and end-user-computing systems into documented, testable transformation logic over governed tables.
Define and implement data quality, validation, reconciliation, and parity testing practices for migrated pipelines.
Establish data lineage, ownership, and cataloguing, and document rollup and allocation rules when source systems have differing grains.
Implement security and compliance measures for PHI/PII and confidential business data, 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 reliability, issue detection, incident response, and continuous improvement.
Support AI/ML and advanced analytical workflows by provisioning trusted data, scalable infrastructure, and governed consumption patterns.
Design and publish reusable data products, data services, APIs, and consumption layers for downstream applications, analytics platforms, and AI/ML use cases.
Partner with product owners, business stakeholders, architects, data scientists, analysts, and engineering teams to translate requirements into scalable solutions and communicate technical tradeoffs.
Design, develop, and deploy AI-enabled data solutions, support responsible AI use, and use AI-powered development tools to improve engineering productivity and delivery quality.
Mentor data engineers and analysts through design reviews, code reviews, best-practice guidance, and support for improving delivery quality.