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.
Primary 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
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:- 8+ years in data engineering and/or data architecture roles with demonstrated ownership of enterprise-scale data solutions
- 6+ years of data architecture experience: designing modern data platforms, integration patterns, and scalable data delivery approaches
- 3+ years of data modeling expertise (dimensional, normalized, canonical models; conceptual/logical/physical modeling)
- 3+ years of proficiency with Python and SQL; solid engineering discipline (testing, documentation, maintainability)
- 1+ years of experience with cloud data technologies including Snowflake and/or Databricks (Spark-based engineering)
- 1+ years of workflow orchestration experience with Apache Airflow; experience with managed Airflow platforms such as Astronomer
- 1+ years of experience implementing production-grade pipelines (batch and/or streaming), including CI/CD practices, environment promotion, and operational support
- 1+ years of experience leading technical direction, mentoring engineers, and influencing architecture decisions across teams
Preferred Qualifications:- 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
*All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $112,700 - $193,200 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
