• Design, build, and implement data systems to support ML and AI models for our health insurance clients, ensuring strict compliance with healthcare data privacy and security regulations (e.g., HIPAA). • Develop tools for extracting, processing, and profiling diverse healthcare data sources, including EHRs, medical claims, pharmacy data, and genomic data. • Collaborate with data scientists to transform large volumes of health-related and bioinformatics data into modeling-ready formats, prioritizing data quality, integrity, and reliability in healthcare applications. • Build and maintain infrastructure for the extraction, transformation, and loading (ETL) of data from a variety of sources using SQL, AWS, and healthcare-specific big data technologies and analytics platforms. • Ensure data pipelines meet the unique requirements of health, medical, and bioinformatics data processing, including translating complex medical and biological concepts into actionable data requirements.

You could be the one who changes everything for our 28 million members by using technology to improve health outcomes around the world.  As a diversified, national organization, Centene's technology professionals have access to competitive benefits including a fresh perspective on workplace flexibility. Position Purpose: Develops and operationalizes data pipelines to make data available for consumption (reports and advanced analytics), including data ingestion, data transformation, data validation / quality, data pipeline optimization, and orchestration. Engages with the DevSecOps Engineer during continuous integration and continuous deployment. Helps guide the design and implementation of complex data management procedures around data staging, data ingestion, data preparation, data provisioning, and data destruction (scripts, programs, automation, assisted by automation, etc.) Provides guidance to data engineers in the design, development, implementation, testing, documenting, and operating of large-scale, high-volume, high-performance data structures for business intelligence analytics Designs, develops, and maintains real-time processing applications and real-time data pipelines Ensure quality of technical solutions as data moves across Centene’s environments Provides senior level knowledge and insight into the changing data environment, data processing, data storage, and utilization requirements for the company and drives the team toward viable solutions Benefits may be subject to program eligibility. Centene is an equal opportunity employer that is committed to diversity, and values the ways in which we are different. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or other characteristic protected by applicable law. Qualified applicants with arrest or conviction records will be considered in accordance with the LA County Ordinance and the California Fair Chance Act

• Design, build, and scale robust ETL pipelines to support complex data workflows while ensuring high performance, reliability, and adaptability to evolving business needs. • Automate and manage data ingestion from diverse sources (databases, APIs, cloud platforms), ensuring system resilience, fault tolerance, and failover readiness. • Optimize data storage, processing, and retrieval layers to balance performance, scalability, and cost efficiency across the data platform. • Modernize and enhance legacy data systems by identifying gaps, implementing architectural improvements, and aligning solutions with future business requirements. • Lead technical excellence within the data engineering function through mentorship, code reviews, best-practice enforcement, and adoption of advanced tools and frameworks. • Ensure end-to-end data quality, integrity, and governance by implementing validation, monitoring, testing, and compliance-focused data controls. • Collaborate cross-functionally with analytics, product, engineering, DevOps, and business stakeholders to translate requirements into scalable data models and transformations. • Drive a data-driven culture and long-term data strategy by enabling self-service analytics, maintaining clear documentation, leading training initiatives, and contributing to architecture roadmaps and governance policies.

• Monitoring production pipelines, solving incidents, improving stability, and ensuring smooth daily data operations. • Provide incident support, investigation, and resolution • Drive continuous improvements across the production environment • Prevent, detect, and capture data processing failures • Ensure proper communication and documentation of all operational activities • Monitor and report on KPI metrics, including % availability of datasets • Manage operational tickets (creation, assignment, follow‑up, resolution) • Maintain and update the internal Knowledge Base