Data Product Engineer, Data & Analytics

About the role

The central Data & Analytics team at Trimble delivers trusted, governed data products for company-wide reporting through data engineering and analytics practices, working with the Data Platform Team to power the company's data backbone. This role shapes the team's future by building reliable data pipelines, analytical models and reusable data products for BI, internal applications and AI consumers, while improving context and metadata management, the semantic layer and AI-assisted workflows.

Key responsibilities

• Build and scale data and analytics engineering: design, build and improve data pipelines, analytical models and reusable data products.
• Provide technical leadership and set standards for architecture, modeling, orchestration, testing, data contracts, documentation, access control and lifecycle management.
• Shape and operate a governed semantic layer providing consistent metrics and business concepts across reporting, applications, APIs and AI services.
• Improve operational excellence through data quality, observability, monitoring, alerting, incident practices, performance optimization, CI/CD and cost management.
• Expand AI-native practices, including how the team defines work for coding agents and reviews generated designs, code, tests and documentation.
• Deliver solutions end to end, including internal applications, APIs, automation and governed agent interfaces such as MCP servers.
• Improve shared workflows for dashboard deployment, authentication, authorization, access management, onboarding and self-service.
• Apply product judgment to prioritize work, define data and service contracts, and help users discover and consume data products.
• Mentor colleagues through pairing, technical guidance and reviews, and support the adoption of AI-native practices.

Required profile

• 5+ years of experience in data engineering, analytics engineering, software engineering or an adjacent field.
• Strong practical experience with SQL, Python, data modeling, ELT, dbt, orchestration and cloud data services, preferably GCP (BigQuery).
• Experience building and maintaining production APIs, MCP servers and endpoints.
• Ability to communicate technical trade-offs to technical and non-technical stakeholders.
• Ideal candidate: pragmatic, self-directed, product-minded, AI-native in delivery and a strong cross-functional partner.

Required skills

• Data engineering and analytics engineering, including data modeling, ELT, pipeline reliability and maintainability.
• SQL, Python, data modeling, ELT, dbt, orchestration and cloud data services (preferably GCP/BigQuery).
• DevOps: Git, code review, automated testing, CI/CD, x as code, environment management, observability.
• Practical experience directing AI coding agents and validating their outputs.
• Competitive edge: BI and analytics governance, semantic layers, metric stores, data catalogs, internal apps with authentication and role-based access.