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.

Lead the design, development, and optimization of end to end data ingestion, ETL/ELT pipelines, and workflow orchestration across Azure, GCP, and AWS environments Architect and manage large scale data warehouse and lakehouse solutions using Snowflake, BigQuery, and Databricks (PySpark, SQL), ensuring performance, scalability, and cost efficiency Oversee data modeling, normalization, schema design, and ML ready data preparation, enabling high quality downstream analytics and AI/ML workloads Implement and enforce robust data quality checks, validation frameworks, lineage tracking, and metadata management using tools like Unity Catalog, Terraform, Git-based CI/CD Drive engineering excellence across batch and streaming pipelines, ensuring resiliency, fault tolerance, and high throughput for structured and unstructured datasets Collaborate with clinical, analytics, and business stakeholders to gather requirements and translate them into scalable technical solutions aligned to healthcare standards (HL7, FHIR, claims, EMR, ICD/CPT/SNOMED) Perform advanced analysis on healthcare datasets to support insights, decisions, and compliance sensitive workflows Provide hands-on leadership, coordinating engineering tasks, prioritizing work, and ensuring timely delivery with solid quality controls Mentor and guide data engineers, fostering best practices in coding, testing, documentation, DevOps automation, and platform reliability Ensure adherence to data governance, privacy, lineage, access controls, and healthcare regulatory requirements (HIPAA, HITRUST) Drive continuous improvement in data engineering standards, reusable frameworks, automation capabilities, and performance optimization techniques Troubleshoot production data pipelines, perform root cause analysis, and implement long-term fixes and performance optimizations Engage cross-functionally with data science, product, security, and platform teams to ensure end to end integration, platform consistency, and scalable delivery Comfortable managing large teams, senior stakeholders, and distributed cross functional partnerships Comply with the terms of employment, company policies, and directives related to work arrangements and organizational requirements 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 regards 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 Required Qualifications: 9+ years of experience in data engineering, with a solid focus on cloud-native data pipelines Hands-on experience with CI/CD, DevOps for data, and infrastructure-as-code Proven experience with Snowflake, BigQuery, and healthcare data formats (FHIR, HL7, claims, EMR) Solid understanding of Azure and GCP cloud services including Data Factory, Synapse, Cloud Composer, and Google Cloud Storage Expert-level proficiency in Databricks, PySpark, SQL, and data orchestration Proven excellent communication and leadership skills,