Analytics Engineering Supervisor

The Analytics Engineering Supervisor is responsible for partnering with business and IT stakeholders to define, govern, and deliver trusted analytics assets that support enterprise decision‑making s role leads the design and management of semantic models, metric definitions, and analytical frameworks to ensure consistency, accuracy, and scalability across the organization’s analytics ecosystem. This position serves as the primary bridge between business stakeholders and technical data teams, translating business questions into well‑defined KPIs, metrics, and reusable semantic structures.

The role ensures that analytics assets are structured to enable self‑service reporting, reduce duplication, and improve trust in enterprise data. The Analytics Engineering Supervisor owns enterprise standards for semantic modeling and metric definition and partners with Data Engineering to ensure underlying data pipelines and datasets support scalable and reliable analytics solutions. Additionally, this role prepares the organization for future advanced analytics capabilities, including AI and machine learning, by enforcing consistent data definitions, governance practices, and reusable analytical structures.

Although the location of the position is in Newton, NJ, from time to time it may be required to undertake duties at other Thorlabs locations.

• Define, design, and oversee enterprise semantic and analytical models, ensuring consistency in metric definitions, aggregation logic, and data interpretation across business units.
• Serve as the primary analytics partner to business stakeholders by translating business questions into clearly defined KPIs, metrics, and analytical frameworks.
• Own and govern enterprise metric definitions, ensuring alignment across departments and resolving discrepancies in KPI interpretation in partnership with business leadership.
• Facilitate working sessions with subject matter experts (SMEs) to align on business definitions, assumptions, and success criteria for analytics initiatives.
• Partner with Data Engineering to ensure data pipelines, transformations, and datasets support scalable, high‑quality, and well‑structured analytical models.
• Help establish and maintain governance processes for semantic models, KPI definitions, and certified datasets, including documentation, validation, and change management.
• Enable business teams to develop their own reporting and analytical solutions by providing trusted, well‑documented, and reusable data assets.
• Drive adoption and trust in enterprise data through transparency, validation processes, and effective communication of analytics standards and definitions.
• Identify and implement opportunities to improve analytics capabilities, data literacy, and business engagement with enterprise data platforms.
• Collaborate with Global IT and cross‑functional teams to align analytics assets with enterprise data platform architecture and strategic priorities.
• Support research and development initiatives related to analytics, including knowledge sharing, documentation, and preparation of data structures for advanced analytics and AI/ML use cases.
• Provide leadership, coaching, and performance management for analytics personnel, including goal setting, performance reviews, and professional development.

• Meeting company standards pertaining to quantity and quality of work performed on an ongoing basis, performing all work related tasks in a manner that is in compliance with all Company policies and procedures.
• Adhering to Company policies, procedures, and directives regarding standards of workplace behavior in completing job duties and assignments.
• The Company retains the right to change or assign other duties to this position.

This is largely a sedentary role; however, some filing is required. This may include lifting files, opening cabinets, bending, or standing as necessary.

• 5+ years of experience in analytics engineering, data analytics, business intelligence, or related roles.
• Experience designing semantic or analytical models (e.g., star schema, tabular models, Power BI datasets).
• Strong…