Associate Data Engineering Manager

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 inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. Primary Responsibilities: Design, build, and maintain scalable data pipelines and data platforms using modern programming languages, cloud data warehouses, and distributed data processing frameworks Develop resilient data architectures across data warehouses, data lakes, Lakehouse platforms, and streaming environments to support enterprise reporting, business intelligence, advanced analytics, and machine learning use cases Lead end-to-end delivery of analytics data products, from requirements understanding and solution design through development, deployment, production support, monitoring, and performance optimization Serve as a Scrum Master / Agile delivery lead for data engineering workstreams by facilitating sprint planning, daily stand-ups, backlog refinement, sprint reviews, retrospectives, and release planning Perform PMO and delivery governance activities including project tracking, dependency management, RAID management, sprint progress reporting, stakeholder updates, and timely escalation of risks, issues, and blockers Collaborate with business stakeholders, product owners, data engineering teams, analytics teams, and data science partners to translate business needs into scalable, secure, and reusable data solutions Ensure data security, governance, quality, lineage, standardization, and compliance are embedded across data pipelines, platforms, and analytics data products Build, optimize, and support ETL/ELT pipelines, data models, orchestration workflows, and reusable data assets that improve reliability, performance, and maintainability Drive continuous improvement in engineering practices through code reviews, technical documentation, automation, CI/CD adoption, monitoring standards, and platform optimization Mentor and guide junior data engineers by sharing best practices, supporting technical problem-solving, and promoting a culture of ownership, collaboration, and delivery excellence Create and deliver clear technical updates, delivery status summaries, and stakeholder communications for both technical and non-technical audiences, ensuring transparency and alignment across teams Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Builder Responsibilities: Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows, and decision-making Required Qualifications: Bachelor's or master's degree in computer science, Engineering, Information Systems, Data Science, or a related technical field 7+ years of relevant experience in Data Engineering, Analytics Engineering, Data Platform Development, or enterprise data solution delivery Solid hands-on expertise in Python and/or Scala/Java for building scalable data pipelines, automation frameworks, and production-grade data solutions Experience designing, building, and optimizing ETL/ELT pipelines across batch and streaming data environments Solid experience with modern big data and distributed processing technologies such as Apache Spark, Databricks, Snowflake, AWS Redshift, and/or Kafka Extensive experience working with Azure cloud services and cloud-native data platform capabilities Solid understanding of data architecture concepts, including data modeling, dimensional modeling, warehousing, Lakehouse architecture, metadata management, and data governance Experience with orchestration, DevOps, and engineering tools such as Airflow, CI/CD pipelines, Git, Linux, and Shell Scripting Hands-on experience working in Agile/Scrum delivery models, including sprint execution, backlog coordination, dependency tracking, risk/issue management, and stakeholder reporting Advanced SQL skills, including complex query development, data manipulation, performance tuning, optimization, and large-scale data validation Solid communication, facilitation, and stakeholder management skills, with the ability to bridge technical teams and business stakeholders effectively Demonstrated ability to work independently and collaboratively in a deadline-driven environment, with Solid ownership, problem-solving, critical thinking, and delivery orientation Ability to mentor junior engineers and influence engineering best practices without requiring heavy people-management responsibilities Preferred Qualifications: Scrum Master, SAFe, PMP, Azure, Databricks, Snowflake, or related cloud/data engineering certifications Experience serving as a Scrum Master, Agile Delivery Lead, Technical Project Lead, or PMO coordinator for Data Engineering or Analytics teams Experience supporting healthcare, payer, provider, pharmacy, or enterprise healthcare analytics data ecosystems Working knowledge of BI and analytics consumption patterns, including how data engineering solutions enable Power BI, advanced analytics, and machine learning use cases Exposure to AI/ML or Generative AI-enabled data engineering use cases, including automation, metadata generation, data quality acceleration, or analytics engineering productivity improvements At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission. #NIC #NJP