Manager, Client Data Engineering

The Manager, Client Data Engineering is a key leader within our TechOps / Technical Implementation organization, leading a globally distributed team of approximately 10–12 data engineering professionals. This role bridges client delivery, technical execution, and people leadership, with a strong focus on successful client implementations and scalable, high-quality data engineering practices.

Your day to day

• Lead Client Implementations: Manage end-to-end client implementations, ensuring timely, high-quality delivery aligned with client expectations.
• Guide Data Engineering Execution: Provide technical leadership across ETL, data mapping, and integration workflows, promoting consistency and best practices.
• Optimize Interoperability Workflows: Work with stakeholders to improve CMS Interoperability and related implementation processes.
• Manage Global Teams: Partner with offshore and distributed teams to coordinate delivery, align priorities, and maintain operational rigor.
• Coach and Develop Talent: Mentor engineers and new hires while building a collaborative, performance-driven team culture.
• Own Operational Execution: Support delivery management, resource planning, and prioritization with delivery and product teams.
• Set and Track Performance Metrics: Define team success measures, monitor progress, and drive continuous improvement.
• Encourage Innovation: Promote practical adoption of tools, technologies, and approaches that improve scalability, quality, and efficiency.

What you bring to the team

• Data Engineering Experience: 7+ years of experience in data engineering roles, including client‑facing implementations.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• Technical Leadership: Demonstrated experience leading and mentoring technical teams.
• ETL & Data Integration Expertise: Strong hands‑on experience designing and supporting ETL workflows and complex data integrations.
• Programming & Analytics Skills: Proficiency with Python and PySpark for data processing and transformation.
• Advanced SQL: Strong ability to write, optimize, and manage complex queries across large datasets.
• Cloud & Data Platforms: Experience working with AWS‑based architectures; familiarity with Databricks and Snowflake.
• Version Control & Collaboration: Practical experience using GitHub or GitLab for code management and collaboration.
• Communication & Problem‑Solving: Clear communicator with a data‑driven approach to solving complex technical and operational challenges.
• AI Experience: Experience working with GenAI and agentic development approaches.

What we would like to see, but not required

• Working knowledge of healthcare data environments and standards, including clinical or claims data, healthcare analytics, payer systems, or healthcare operations.
• Familiarity with data visualization tools such as Tableau or Power BI.
• Experience applying DevOps or CI/CD practices in data engineering environments.

What you’ll receive in return

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee, giving you a share in our success
• A growth-focused environment where your development matters

Work Arrangements

• Standard hours: 9 hours/day, 5 days/week
• Location: On-site
• Work hours: 10 AM–7 PM, with some variation based on business needs.