Manager, Data Engineering - Archimedes

The Manager, Data Engineering is responsible for leading the design, implementation, operation, and modernization of the organization's enterprise data platform, lakehouse architecture, data integration ecosystem, and AI-ready data foundation. This role provides both technical leadership and people leadership across Data Engineering, Data Integration, DataOps, and enterprise data modernization initiatives. Operating within an Azure-first, Databricks-centric environment, the Manager, Data Engineering leads the organization's transition from traditional SQL-centric ETL architectures toward modern cloud-native lakehouse platforms utilizing Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2, Azure Data Factory, APIs, event-driven architectures, and modern DataOps practices. This is a hands-on leadership role responsible for establishing enterprise data architecture standards, canonical data models, master data management strategies, data governance controls, data quality frameworks, integration patterns, and AI-ready data products supporting analytics, machine learning, intelligent automation, robotic process automation (RPA), generative AI, and operational decision-making.

The Manager, Data Engineering directly leads Data Engineers and Data Integration Engineers while remaining actively engaged in architecture, design reviews, platform modernization, solution delivery, and technical mentoring.

The role partners closely with Software Engineering, Cloud Engineering, DevOps, Security, Analytics, Compliance, and business stakeholders to deliver scalable, secure, governed, and reusable enterprise data assets.

The Manager, Data Engineering is accountable for both current-state ETL and integration operations as well as the long-term transformation toward cloud-native data platforms, lake house architectures, enterprise data products, and AI-enabled business capabilities.

How do I make an impact on my team ?

• 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.