Role Description We are seeking a highly motivated Manager - Data Engineering to join our growing team. This role will report to the Director of Data Engineering and will lead a team of data engineers. The ideal candidate is a strong technical leader with a passion for mentoring others and delivering scalable, high-quality data solutions. You will collaborate closely with data analysts, data scientists, technology teams, and business leaders to design and implement robust data pipelines and platforms that support internal and external clients. With over a million managed lives across the country and terabytes of healthcare data generated, your leadership will be critical to empowering our organization with the tools and insights needed to drive innovation and improve patient outcomes. What You'll Do - Lead and mentor a team of data engineers, fostering a culture of continuous learning, innovation, and operational excellence. - Partner with cross-functional teams to design and implement scalable data pipelines and models aligned with business needs and industry best practices. - Oversee the development, deployment, and maintenance of enterprise-grade ELT processes, data marts, and business intelligence assets. - Own and evolve data quality frameworks, ensuring proactive monitoring, troubleshooting, and resolution of data issues. - Champion the adoption of automation and modern data engineering practices, driving efficiencies in pipeline management and data delivery. - Guide your team in building domain knowledge around healthcare operations, ensuring data solutions comply with regulatory and security requirements. - Contribute to strategic planning for the data engineering function, including resource allocation, technology selection, and process improvement initiatives. - Support and enforce software development best practices, including code reviews, source control, CI/CD, and testing. Qualifications - Bachelor’s degree required in computer science, engineering, healthcare, analytics, or a related field; Master’s degree preferred. - 5+ years of experience in data engineering or software development roles, with at least 2+ years in a leadership or management capacity. - 2+ years of experience with medical claims in healthcare and/or managed care. - Strong expertise in building scalable ELT/ETL pipelines using modern data platforms and cloud-based services (e.g., AWS, Azure). - Advanced proficiency with SQL and scripting/programming languages such as Python or Spark. - Solid understanding of database structures, data modeling (normalized, star schema, snowflake), and data warehousing concepts. - Experience working with Databricks or similar distributed computing platforms preferred. - Familiarity with BI tools such as Power BI. - Deep understanding of professional software engineering practices including SDLC, version control, CI/CD, and automated testing. - Strong communication and stakeholder management skills, with the ability to translate technical concepts for non-technical audiences. Requirements - Thrive in a fast-paced environment and enjoy leading teams to solve complex data challenges. - Passionate about mentoring and growing the next generation of data engineering talent. - Have a bias for action and a commitment to delivering high-quality solutions with measurable impact. - Bring a collaborative, team-first mindset to everything you do. Benefits - This position is remotely based in the U.S. - The total compensation target pay range for this role is: $140,000 - $160,000. Actual compensation will be determined based on geographic location (current or future), experience, and other job-related factors. Company Description Astrana Health is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. All employment is decided based on qualifications, merit, and business need. If you require assistance in applying for open positions due to a disability, please email us at humanresourcesdept@astranahealth.com to request an accommodation. The job description does not constitute an employment agreement between the employer and employee and is subject to change by the employer as the needs of the employer and requirements of the job change.

Role Description This role will primarily become proficient with all internal & external data produced and consumed by Hanover’s Core Commercial and Specialty businesses. This role will drive complex projects independently as well as under the mentorship of experienced engineers. The Associate Data Engineer will collaborate with business partners and cross-functional teams on data and reporting requests. The assignments will become broader in nature, usually requiring originality and ingenuity. This role will be accountable for successful outcomes related to designing, developing, implementing, optimizing, and maintaining data integrations, pipelines and solutions. In this role, you will: - Complete data integration and build scalable ETL pipelines - Learn data engineering best practices for data storage, integration, transformation, and access within cloud and on-premises platforms - Gain a strong understanding of Medallion Architecture and Data Lake best practices - Ensure data security and compliance within industry standards - Develop and maintain documentation for data integration and ETL solutions - Build solutions within Azure Data Services (Azure Synapse Analytics, Azure SQL Database, Azure Data Lake Storage) - Create solutions using pySpark/SparkSQL, and Delta Tables - Collaborate with data scientists, analysts, and stakeholders - Work within agile development methodologies - Occasional Power BI report creation for data delivery - Utilize best practices for CI/CD Qualifications - Bachelor’s degree or higher in Data Science, Mathematics, Statistics, Computer Science, Information Systems, Business Information Technology, or other related fields - 0-2 years professional experience in data engineering, data architecture, and/or data analytics - Beginner proficiency in SQL and Python - Strong problem-solving, communication, and collaboration skills - Critical thinking and attention to detail - Must be eligible to work in the United States without requiring sponsorship now or in the future (i.e. US Citizen or green card holder) Requirements - Some experience working with Azure, AWS, GCP, or other cloud - CI/CD experience with ADO or Github - Azure Synapse Analytics and/or Microsoft Fabric experience - PowerBI experience - Insurance industry experience Benefits - Medical, dental, vision, life, and disability insurance - 401K with a company match - Tuition reimbursement - PTO - Company paid holidays - Flexible work arrangements - Cultural Awareness Day in support of IDE - On-site medical/wellness center (Worcester only)

• Analyze and consolidate historical workforce, FTE, and timekeeper data from multiple source systems. • Design and develop automated data conversion and transformation processes to reduce manual effort and improve data quality. • Extract data from APIs, databases, flat files, and other structured and unstructured sources. • Perform data cleansing, standardization, validation, and enrichment activities to ensure data integrity. • Transform and map source data to align with target system requirements and reporting standards. • Produce standardized output files and datasets that conform to the target application's native data model. • Partner with business stakeholders to gather requirements and translate business needs into technical data solutions. • Conduct data quality assessments, reconciliation, and validation activities throughout the migration lifecycle. • Support testing, dry-run conversions, and production migration activities. • Troubleshoot data issues and implement corrective actions as needed. • Document data mappings, transformation rules, conversion logic, and validation procedures. • Collaborate with project managers, business analysts, and client stakeholders to ensure successful project delivery.

• Deliver production-ready datasets and pipelines to support data science and analytics. • Bridge the gap between ML experimentation and deployment with MLOps best practices. • Build and deploy data-driven applications using Azure services. • Maximize ROI on Azure investment through cloud-native architecture. • Standardize development workflows using GitHub (version control, pull requests, CI/CD). • Automate deployments and accelerate engineering velocity with GitHub Actions. • Reduce bottlenecks for analysts, engineers, and data scientists by improving data accessibility and workflow efficiency. • Enhance data quality, governance, and observability. • Enable future capabilities such as AI, automation, and personalization.