Drive Innovation as our Next Data Product Engineer
- Data & Analytics Ready to participate in shaping the next evolution of our central Data & Analytics team as we elevate our reporting and data-product foundation for the agentic AI era? The central Data & Analytics team delivers trusted, governed data products for company-wide reporting through data engineering and analytics practices. In partnership with the Data Platform Team, we power the company’s data backbone and help ensure data supports day-to-day operations and decision‑making. To improve human and agentic AI data consumption, we are enhancing context and metadata management, extending our semantic layer, and strengthening deployment and AI‑assisted workflows so these services remain governed, scalable, and resilient. Working alongside experienced colleagues, you will influence direction and build solutions that turn promising ideas into durable data products, capabilities, and services. Your

Key Responsibilities
Build & scale data and analytics engineering Design, build, and improve reliable data pipelines, analytical models, and reusable data products for BI, internal applications, and AI consumers. Provide opinionated technical leadership and establish standards for architecture, modeling, orchestration, testing, data contracts, documentation, ownership, access control, and lifecycle management. Improve operational excellence through data‑quality controls, observability, monitoring, alerting, incident practices, performance optimization, CI/CD, and cost management. Expand AI-native practices & solution delivery Evolve the team’s AI‑native practices, including how it defines work for (coding) agents and reviews generated designs, code, tests, and documentation.



Deliver solutions end to end—from discovery and requirements through implementation, deployment, operation, and iteration—including internal applications, APIs, automation, and governed agent interfaces such as MCP servers. Improve shared workflows for programmatic dashboard deployment, authentication and authorization, access management, onboarding, and self‑service, partnering with platform and security specialists where appropriate. Help users discover and consume data products and semantic models, clearly communicating definitions, ownership, freshness, quality, limitations, and appropriate validation. Define explicit data and service contracts with dependent teams, including ownership, interfaces, service expectations, versioning, and escalation paths. Mentor colleagues through pairing, technical guidance, and thoughtful reviews across data engineering, analytics engineering, and solution delivery. Help the team consistently apply agreed standards for modeling, testing, code review, deployment, observability, documentation, and governance. Evaluate relevant developments in data, software engineering, and AI tooling, helping the team adopt practices that simplify delivery and improve outcomes. 5+ years of experience in data engineering, analytics engineering, software engineering, or an adjacent field. ~ Demonstrate mastery of data engineering and analytics engineering, including data modeling and ELT design, pipeline performance, reliability, and maintainability.



Deep practical experience with SQL, Python, data modelling, ELT, dbt, orchestration, and cloud data services—preferably using GCP (BigQuery), dbt, Airflow. ~ Strong DevOps practices: Git, code review, automated testing, CI/CD, x as code, environment management, observability. ~ Practical experience designing workflows for and directing AI coding agents, and validating generated designs, code, tests, and documentation. ~ Experience building and maintaining production APIs, MCP servers, and endpoints. ~ The ideal candidate is a pragmatic, self‑directed builder who owns data products end to end and improves the analytics platform through strong data/analytics engineering and product judgment. They treat testing, observability, documentation, and governance as core product work, and they build trusted, resilient, cost‑effective systems (pipelines, models, semantic layer, contracts, CI/CD). They are curious, opinionated but evidence‑driven, AI‑native in delivery (clear PRDs, effective use of coding agents, rigorous review), and a strong cross‑functional partner who communicates trade‑offs and enables correct data consumption. Experience with BI and analytics governance, including metric consistency, semantic modeling, access control, data quality, discoverability, lifecycle management, and cost. Experience implementing semantic layers, metric stores, data catalogs, or governed analytical interfaces.

About Us
Trimble is a integral technology company that connects the physical and digital worlds, transforming the ways work gets done. With relentless innovation in precise positioning, modeling and data analytics, Trimble enables essential industries including construction, geospatial a

📌 Data Product Engineer, Data (España)
🏢 Trimble
📍 España