• Create and manage ETL processes using Azure Data Factory to automate data flow between various systems and services • Implement data storage solutions using Azure Data Lake and Cosmos DB, ensuring high availability and scalability • Design and optimize data processes in Databricks, implementing Spark jobs that utilize Delta Lake for efficient data handling and versioning • Build and maintain analytics models that can harness the capabilities of Delta Lake for better data quality and performance • Develop scripts in Python and PowerShell to automate operational tasks, deployments, and monitoring of data workflows • Collaborate with development teams to integrate CI/CD practices using Azure DevOps and Git for version control • Manage and optimize MS SQL Server databases, writing efficient T-SQL queries for data manipulation and reporting • Ensure data integrity and security through proper database management practices • Implement logging and monitoring solutions to proactively address any issues impacting data applications and workflows • Conduct root cause analysis on data-related problems and develop solutions to mitigate future occurrences • Collaborate with business intelligence teams to create and share reports using Power BI • Provide insights and recommendations based on data analysis to guide business decisions

• Design, build, and own scalable, resilient data pipelines and data products • Power the core of Content Finance planning and systems • Answer questions regarding content budgets for upcoming forecasting seasons • Define modern data engineering practices • Establish strong data foundations and data modeling • Influence how GenAI is leveraged in the data engineering ecosystem • Shape decision-making on funding and content business growth

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description A Lead Data Engineer collaborates with the Data Quality and Governance team to ensure data pipelines, data integrity, and compliance by defining data strategies, implementing Data Governance capabilities, creating self-service data assets, and integrating robust data quality and governance frameworks to support trustworthy AI solutions. - Design scalable data architectures - Establish data quality standards and monitoring - Manage data lineage - Ensure adherence to regulatory requirements and privacy policies Qualifications - Bachelor's Degree Requirements - 8+ years of data management experience - Prior experience leading projects or teams - Strong experience on Data Lake, Data Warehouse, Data Validation & Certification, Data Quality, Metadata Management and Data Governance - Experience in Azure Data Services (like Azure Databricks, Unity Catalog, Purview, Azure Data Factory) and Power BI - Experience in programming languages such as PySpark/Python/SQL - Preferred experience in building stream-processing systems, using solutions such as Kafka, Storm or Spark-Streaming - Experience in implementing AI based solutions in Data Governance space Benefits Molina Healthcare offers a competitive benefits and compensation package. Company Description Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.

• Lead and serve as the subject matter expert in the design, development and delivery of data pipelines and value-added data assets across the OU Health data ecosystem • Design, build, and maintain data pipelines that deliver curated, value-added data assets such as data marts and other purpose-built data stores • Ensure data pipelines are optimized, highly reliable, and contain low technical debt • Design, build, and maintain the tools and infrastructure needed to handle large datasets • Enforce data governance policies including data quality, validation, lineage, metadata management, and adherence to healthcare regulations • Develop and implement comprehensive data quality frameworks, addressing issues such as data accuracy, completeness, and consistency • Work closely with different application and operational teams to understand business needs and align data engineering initiatives accordingly • Guide, mentor, quality review and train Data Engineering team and ETS department on technical skills and best practices