Manager, Data Engineering

Responsibilities

• Lead and support the organizational data integration efforts by effectively developing and leading a team of data integration developers, engineers, architects, and managers.
• Establish enterprise data architecture standards, canonical data models, data domains, and data product strategies.
• Lead the modernization of legacy SQL Server ETL workloads into Azure Databricks and Lakehouse architectures.
• Define and govern Bronze, Silver, and Gold data layer standards.
• Establish enterprise data dictionaries, business glossaries, metadata management, and lineage standards.
• Lead development of AI‑ready data products supporting machine learning, predictive analytics, intelligent automation, RAG, and agentic AI solutions.
• Define enterprise Data Ops practices including CI/CD, automated testing, observability, data quality, and deployment automation.
• Lead the design and implementation of data integration and data lake house solutions.
• Lead collaboration efforts with IT teams to ensure robust and scalable data architecture is established and meeting company objectives.
• Lead the establishment of data validation and reconciliation processes to maintain data accuracy.
• Partner with business stakeholders to understand data requirements and deliver solutions that meet their needs.
• Assess the current data services processes, identify challenges, quantify the business value, establish procedures to address challenges, and support the future state model and vision definition.
• Stay current with industry trends and advancements in data integration and management technologies.
• Lead healthcare data integration initiatives involving claims, eligibility, pharmacy, clinical, financial, operational, and partner data sources.
• Serve as technical authority for data modeling, canonicalization, master data management, and enterprise data governance practices.
• Provide direct leadership, coaching, hiring, and performance management for Data Engineers and Data Integration Engineers.
• Participate in architecture reviews and remain actively engaged in solution design, platform modernization, and technical delivery.
• Develop training plans to foster growth and development across functional areas to meet expanding technology needs.
• Research and develop learning needs for ongoing system developments with contractors.
• Continuously review and monitor technology resources and gap analysis, establish criteria and make recommendations for advancement.
• Develop and implement a comprehensive data management strategy aligned with strategic objectives.
• Establish resources, tools and direction for each functional leader.
• Establish data integration policies and procedures to ensure data accuracy, security, and compliance with regulatory requirements.
• Participate in, adhere to, and support compliance, people and culture, and learning programs.
• Perform other duties as assigned.

• Bachelor’s degree in the field of computer science, information systems, or data science required.
• 10+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, Data Integration, or Data Platform Engineering required.
• 5+ years leading Data Engineering teams required.
• Experience designing and implementing Databricks Lakehouse architecture required.
• Experience establishing canonical data models, enterprise data products, metadata management, and governance frameworks required.
• Experience supporting AI, machine learning, analytics, and automation initiatives through modern data engineering practices required.
• Experience modernizing legacy ETL and SQL-based architectures into cloud‑native platforms required.
• Experience with healthcare data domains strongly preferred.
• Advanced experience and skills in data ingestion, data architecture, and data integration techniques required.
• Proficiency in data integration tools and languages (e.g., SQL, Linux, Python, ETL tool) required.

• data integration
• data architecture
• data modeling
• ETL
• Azure Databricks
• data governance
• data quality
• CI/CD
• machine learning
• predictive analytics

• leadership
• coaching
• collaboration
• communication
• problem‑solving
• performance management
• training development
• strategic planning
• stakeholder engagement
• team management

• Bachelor's degree in computer science
• Bachelor's degree in information systems
• Bachelor's degree in data science