• Build and maintain data pipelines that power analytics, ML workloads, and product-facing applications • Evolve our data platform alongside cloud platform engineers • Design and implement scalable data infrastructure • Develop data services and APIs that expose trusted data to product applications • Own data quality and observability • Partner with AI engineers, data scientists, analysts, and product teams • Uphold strong data privacy, security, and compliance practices • Create and maintain comprehensive documentation of data flows, models, and systems

Role Description The Data Engineer will be responsible for the following duties: - Review source to target documents and create testing scripts to ensure data is being loaded correctly into the National Data Warehouse. - Recognize problems to make recommendations/decisions on the best course of action to remediate. - Execute planned testing, document testing results, and track defects. - Meet with business and technical teams to discuss functional design concepts and alternatives necessary to meet requirements. - Summarize testing and validation results to communicate and make recommendations/decisions on the best course of action to remediate. - Create solutions using existing or available resources based on knowledge of the organization and level of execution effort. - Define acceptance test objectives, test cases, and assist in system and integration testing. - Support practices, policies, and operating procedures and ensure alignment to departmental objectives and strategy. - Ensure each level of the organization has the information needed and the context needed to produce favorable results. - Build strong relationships with IT operational leaders to ensure connectivity to the business. Telecommuting is available from anywhere in the U.S. Qualifications - Master’s degree in Computer Science, Information Technology, Information Management, or related field. - Completion of a graduate-level/university-level course, research project, internship, or thesis. - One (1) year of experience involving the following: Requirements - Cloud Platforms such as Snowflake, Azure Databricks, or Microsoft Azure. - Data Pipeline Awareness. - Databricks. - SQL. - Delta Lake. - Data Modeling and Warehousing Concepts. - Data Visualization. - GitHub. - Scala. - Python. - Power BI. - Tableau. Benefits - Full range of comprehensive benefits, including medical, dental, and vision. - Matching 401k. - Employee stock purchase plan. Company Description Optum, part of the UnitedHealth Group family of businesses, is focused on making health care work better for everyone. The organization aims to leverage health-related information and guide health and lifestyle choices while managing pharmacy benefits for millions. Optum brings together advanced ideas and high-performance teams to tackle sophisticated challenges in health care.

• Design and implement robust, scalable data pipelines for structured and unstructured data. • Oversee ETL/ELT processes to ingest data from core banking systems, CRM, and external sources. • Ensure data models support real-time analytics, reporting, and machine learning. • Collaborate with data scientists and analysts to provide usable, cleaned and aggregated data to end users • Ensure data practices comply with banking regulations (e.g., Basel III, GDPR, local laws). • Implement data governance frameworks, including metadata management and lineage tracking. • Collaborate with cybersecurity teams to safeguard sensitive financial data. • Monitor and optimize data infrastructure for performance, reliability, and cost-efficiency. • Troubleshoot data issues and ensure high availability of data services. • Evaluate and adopt modern data technologies (e.g., cloud platforms, data lakes, streaming tools). • Promote automation and DevOps practices in data engineering workflows.

• Design and maintain Snowflake data models for customer engagement and audience activation • Build customer-level data structures for segmentation and lifecycle marketing • Create and maintain data pipelines for syncing with downstream tools • Support clients in building customer 360 views across channels • Ensure data quality and compliance in customer-facing data models