Data Architect / Data Engineering Lead

UnitedHealth Group•Minnetonka, MN

18h•$112,700 - $193,200•Remote

About The Position

Explore opportunities with Logistics Health Incorporated (LHI), part of the Optum family of business. We’re dedicated to simplifying the logistics of complex workforce health programs with cost-effective solutions and a seamless distribution process. With offices in La Crosse, Wis., a satellite office in Chicago and remote employees throughout the country, we have a variety of rewarding career opportunities for you. Elevate your career as you help us create a healthier tomorrow for everyone and discover the meaning behind Caring. Connecting. Growing together. We are seeking a Data Architect / Data Engineering Lead to own the end-to-end architecture and engineering standards for modern data platforms. This role partners closely with product, analytics, and engineering teams to design scalable, secure, and cost-effective data solutions—while remaining hands-on with implementation where it matters most. This position blends data architecture leadership (roadmaps, standards, patterns, governance) with data engineering execution (pipelines, modeling, orchestration, performance optimization), ensuring data is delivered reliably and with high quality.

Requirements

8+ years in data engineering and/or data architecture roles with demonstrated ownership of enterprise-scale data solutions
Expert-level data architecture experience: designing modern data platforms, integration patterns, and scalable data delivery approaches
Solid data modeling expertise (dimensional, normalized, canonical models; conceptual/logical/physical modeling)
Advanced proficiency with Python and SQL; strong engineering discipline (testing, documentation, maintainability)
Proven experience with cloud data technologies including Snowflake and/or Databricks (Spark-based engineering)
Solid workflow orchestration experience with Apache Airflow; experience with managed Airflow platforms such as Astronomer
Experience implementing production-grade pipelines (batch and/or streaming), including CI/CD practices, environment promotion, and operational support
Demonstrated ability to lead technical direction, mentor engineers, and influence architecture decisions across teams
If you are offered this position, you will be required to provide extensive personal information to obtain and maintain a suitability or determination of eligibility for a Confidential/Secret or Top Secret security clearance as a condition of your employment

Nice To Haves

Bachelor’s degree in Information Technology, Computer Science or related field
dbt experience (modeling and transformation lifecycle management)
Experience with Snowpark framwork
Experience with data governance/metadata tools and practices (catalog, lineage, access policies)
Observability tooling experience (pipeline monitoring, alerting, logging, operational dashboards)
Infrastructure-as-code and containerization (Terraform, Docker, Kubernetes) in support of data platforms
Experience supporting regulated data environments (e.g., healthcare/PII/PHI) and security-by-design patterns

Responsibilities

Define and evolve target-state data architecture, including reference architectures, integration patterns, and platform standards across warehouse/lakehouse ecosystems.
Lead architectural decision-making for cloud data platforms (e.g., Snowflake, Databricks) including compute/storage patterns, multi-environment strategies, security boundaries, and cost controls.
Create technical roadmaps that align business outcomes with scalable data capabilities (analytics, operational reporting, AI/ML readiness).
Own enterprise and domain data modeling standards (conceptual/logical/physical), including dimensional modeling, canonical models, and patterns for curated datasets.
Ensure consistent definitions, metrics alignment, and high-quality, analytics-ready data products.
Lead design and build of robust ELT/ETL pipelines using Python and distributed processing (Spark) as needed.
Drive performance tuning and optimization across pipelines and data platforms (query patterns, clustering/partitioning, incremental loads, caching strategies).
Establish engineering practices that improve reliability, maintainability, and developer productivity.
Design and operationalize workflow orchestration using Apache Airflow, including DAG standards, scheduling patterns, dependency management, retries, SLAs, and backfills
Implement and manage Airflow runtime patterns in managed platforms such as Astronomer (or equivalent), including environment promotion strategies and operational readiness
Define and implement data quality frameworks (validation checks, anomaly detection, reconciliation, data contracts) and operational monitoring
Partner with governance/security stakeholders to ensure compliant data handling, access controls, lineage, metadata, and auditability
Lead/mentor engineers and architects through design reviews, code reviews, and architecture governance forums; raise the bar on engineering excellence
Translate complex technical concepts into clear guidance for stakeholders; drive alignment on tradeoffs, timelines, and expected outcomes