Senior Data Engineer

UnitedHealth Group•Eden Prairie, MN

1d•Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. You will enjoy the flexibility to telecommute from anywhere within the U.S. as you take on some tough challenges.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or IT related field
7+ years of experience in data modeling, database design, and data architecture patterns
7+ years of experience with SQL Server, SQL MI, Azure SQL, PostgreSQL, MongoDB
7+ years of experience in performance tuning, indexing, partitioning, and workload optimization
7+ years of experience with Metadata, lineage, quality, RBAC, encryption, masking, compliance
4+ years of experience with Azure Databricks, Synapse, ADF
4+ years of experience with Terraform, GitHub Actions, DevOps, Kubernetes/AK

Nice To Haves

Strong analytical, communication, and leadership skills

Responsibilities

Define and own the end‑to‑end enterprise data architecture ensuring scalability, performance, integration, and long‑term sustainability
Develop and maintain reference architectures, patterns, and standards for data modeling, storage, ingestion, processing, governance, and consumption
Align data platforms with business outcomes, regulatory requirements, cloud strategy, and modernization initiatives
Evaluate and recommend technologies across SQL/NoSQL databases, data lakes, lake houses, ETL/ELT tools, and compute engines based on enterprise fit
Lead the design of conceptual, logical, and physical data models to support OLTP, OLAP, streaming, and AI workloads. Architect robust database schemas, partition strategies, indexing policies, sharding methods, and storage optimizations
Define standards for naming, modeling conventions, normalization/denormalization, and lifecycle management of data assets
Provide expert guidance on SQL Server, SQL MI, Azure SQL, PostgreSQL, MongoDB, and other enterprise data platforms
Architect and implement cloud-native, hybrid, and on-prem data platforms supporting ingestion, storage, processing, and analytics
Design lake house and warehouse environments using Databricks, Synapse, ADF, Delta Lake, and scalable compute layers
Build end-to-end data flows covering batch, streaming, ELT pipelines, metadata, lineage, and monitoring
Ensure data platforms meet performance, concurrency, high-availability, and disaster recovery requirements
Define policies for metadata management, classification, lineage, quality scoring, and stewardship
Enable enterprise governance across catalogs, glossaries, access patterns, and regulatory compliance
Enforce security controls such as RBAC, encryption, auditing, masking, and network isolation
Establish data quality standards including validation rules, anomaly detection, and reconciliation workflows
Implement infrastructure and data platform provisioning using Terraform/IaC for repeatable deployments
Build CI/CD pipelines (GitHub Actions, Azure DevOps) for data models, SQL objects, ETL workflows, and platform components
Support containerized workloads using Docker and Kubernetes/AKS
Automate environment setup, schema migrations, monitoring, and compliance checks
Define performance benchmarks and baselines across storage, computer, SQL, and data movement
Optimize SQL/ETL workloads, reduce infrastructure costs, and tune pipelines for high throughput
Establish monitoring, alerting, anomaly detection, and reliability playbooks
Lead incident triage, RCA, and long-term remediation for platform stability
Partner with Product, Engineering, Analytics, Security, and Cloud teams to align data strategy with business goals
Translate business requirements into scalable data solutions
Provide mentorship and technical leadership to data engineers, DBAs, and architects
Stay current with emerging trends in data modeling, platforms, storage formats, and distributed systems
Drive modernization initiatives such as lake house adoption, automation, and cloud-native architecture
Refine architecture based on usage insights and business evolution
Maintain documentation of architectures, data flows, ER diagrams, standards, and decisions
Conduct reviews, design sessions, and best-practice workshops
Promote organization-wide literacy on governance, modeling, and quality
Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI