The Engineering Manager, Manufacturing Analytics & Business Intelligence leads a team of industrial and business intelligence engineers in developing and implementing solutions that transform manufacturing decision making. This role partners with senior leadership to shape the organization's strategy for data-driven operations, while ensuring the team's structure, frameworks, and resources are aligned to organizational goals. The Engineering Manager is also responsible for elevating the technical engineering skills of the team by instilling rigor in methods, practices, and execution through structured coaching and mentorship. Translate business problems into structured projects that you lead in end-to-end delivery against key objectives, from hypothesis development to final deliverables. Ensure projects progress from scope definition through execution, adoption, and sustainability, with clear milestones and measurable business outcomes. Act as a thought partner and subject matter expert to refine ideas, generate hypotheses, and guide the team in aligning technical solutions with financial, operational, and customer outcomes. Lead analytical workstreams - develop strategic, analytical, and financial frameworks to conduct analyses and solve critical business issues with minimal oversight from functional leaders. Demonstrate strong leadership and influence management skills, including the ability to challenge the status quo and manage key senior stakeholders. Lead, mentor, and develop a high-performance team to manage complex projects and technical initiatives and identify innovative solutions. Invest in the professional development of the engineering team including coaching, performance reviews, and long-term career guidance. Knowledge from high-precision, high-reliability manufacturing environments (e.g., semiconductors, photonics, consumer electronics, contract manufacturing) to ensure decision systems enhance quality, reliability, and operational excellence. Lead the development of measurement systems for monitoring processes and equipment, ensuring sustainable improvements in cycle time, yield, and defect reduction. Oversee the design, governance, and adoption of analytical models, data pipelines, dashboards, and self-service BI platforms (Python, SQL, Tableau, Power BI, etc.). Develop organizational frameworks that ensure BI capabilities are scalable, reliable, and focused on measurable value delivery. Coach engineers on adoption strategies, lifecycle management, and information architecture. Collaborate across manufacturing & warehouse engineering, quality engineering, IT, production operations, and external partners to align data and BI systems with business needs. Oversee the development of data artifacts (facts, dimensions, and metrics) that comprise the information architecture for business decision making. Define the vision & strategy for developing, implementing, and improving the industrial models, simulations, and predictive tools used to drive business planning & decision making. Partner with leadership to define the vision and strategy for AI/ML adoption across manufacturing operations. Structure engineering frameworks for using advanced statistical and analytical methods (regression, correlation, DOE, SPC, PFMEA, Gauge R&R, commonality studies) to identify, quantify, and control risk in complex manufacturing environments.
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Job Type
Full-time
Career Level
Manager
Industry
Professional, Scientific, and Technical Services
Education Level
Bachelor's degree