Manager, Data Engineering - Archimedes

Navitus Health Solutions, through its subsidiary Archimedes, is a leader in specialty drug management solutions. The Manager, Data Engineering will lead the design and modernization of the enterprise data platform, overseeing data integration and AI-ready data initiatives while managing a team of Data Engineers and Data Integration Engineers.

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 DataOps 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

Requirements:

• 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
• 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
• Experience with healthcare data domains strongly preferred