Senior Manager Data Engineering

Position Overview: Leads the design, build, and operation of Bollinger’s enterprise data platform and data engineering function. This role ensures that data from ERP, engineering, and operational systems is integrated, reliable, and accessible to support analytics, forecasting, and AI initiatives across the enterprise. Key Responsibilities

• Define and execute the roadmap for the Azure data platform (ADLS, Synapse) projects
• Oversee integration of Oracle ERP, Finesse, historical proposal data, and MES/PLM systems into a unified data environment
• Ensure consistent implementation of scalable data pipelines and medallion architecture (bronze silver gold)
• Establish standards for data engineering, pipeline development, and data lifecycle management
• Ensure data quality, reconciliation, and consistency across enterprise systems
• Enable delivery of datasets supporting Executive Dashboard and Financial Forecasting
• Support AI and machine learning initiatives through robust and scalable data infrastructure
• Lead and scale the data engineering and platform teams, ensuring disciplined execution and delivery

Engineering Management, Engineering Management, ERP (Enterprise Resource Planning), Forecasting, Artificial Intelligence (AI), Microsoft Windows Azure, Oracle ERP, Product Lifecycle Management, Manufacturing Execution Systems (MES), Data Management, * 12-15+ years experience in data engineering or data platform roles

• Proven experience building and scaling enterprise data platforms
• Strong understanding of data pipelines, data warehousing, and cloud platforms (Azure preferred)
• Experience integrating complex enterprise systems (ERP, operational, engineering systems)
• Strong leadership and execution capabilities in cross-functional environments

Skills & Abilities

• Azure data platform is operational, scalable, and broadly adopted
• Core enterprise systems are consistently integrated into a unified data environment
• Data pipelines are reliable, maintainable, and aligned with enterprise standards

• Analytics and forecasting are enabled by high-quality, timely data

• AI use cases are supported without data bottlenecks or rework

• Data engineering operates as a scalable, disciplined function across the enterprise