Are you passionate about building data infrastructure that powers cutting-edge AI evaluation systems? Thomson Reuters Labs is seeking a Data Engineer to join our AI data, evaluation, and governance team and help shape how we measure, monitor, and improve AI-powered legal products. In this role as a Data Engineer, you will:Build & Deploy Product Analytics Infrastructure: Design and implement scalable data pipelines and analytics systems that transform customer feedback and product traces into actionable insights across AI-powered legal products. Enable AI Evaluation at Scale: Build data workflows to enable the deployment of automated evaluation metrics as production analytics to continuously track product quality, detect errors, and alert teams to regressions before they impact customers. Establish Data Governance & Quality Standards: Develop technical governance infrastructure for manual and automated review of AI product data, particularly for small and medium law firms, ensuring data quality, security, and compliance. Drive Metric Development: Analyze product traces and customer feedback to identify quality issues and patterns that inform the development of new evaluation metrics and feed into product roadmap decisions. Support Cross-Functional Teams: Partner closely with Product Scientists, Research Engineers, and Subject Matter Experts to implement configurations, build reporting dashboards, and create self-service tools for metric implementation. Advance AI Evaluation Best Practices: Contribute to the development and scaling of automated evaluation capabilities and establish best practices for AI evaluation and analytics across Thomson Reuters pillars (Legal, Tax & Accounting, and Reuters News). About You:You are a data engineering professional who thrives at the intersection of infrastructure, analytics, and AI systems. You understand that great AI products require great measurement, and you're excited to build the systems that make that possible. Required Qualifications:- Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or related technical field - 8 years of professional experience in data engineering, analytics engineering, or related roles- Strong programming skills in Python and SQL with experience building production data pipelines- Hands-on experience with modern data stack technologies (e.g., Snowflake, AWS, Power. BI, or similar orchestration, transformation, or analytics tools)- Experience with cloud platforms (e.g., AWS, or similar) and their data services- Proven ability to design and implement scalable ETL/ ELT pipelines for structured and unstructured data- Experience with data warehousing, data modeling, and analytics infrastructure- Strong understanding of data governance, data quality, and security best practices- Excellent communication skills to collaborate with cross-functional teams including scientists, engineers, product managers, and subject matter experts- Self-driven attitude with ability to manage projects independently and meet deadlines-Core Technical Skills (Must-Have)Snowflake, Power BI, SQL, Python. ETL/ ELT pipelines, data lakes, AWS/cloud-Analytics & Data (Critical)Experience with customer data, user behavior, and product analytics. Ability to build dashboards, define KPIs, and deliver insights-ML / AI Data (Required)Experience working with AI/ ML data (prompts, model outputs, evaluation datasets)Ability to analyze and monitor AI performance. Familiarity with LL - Ms is a plus (not required) Not a model-building role — focus is on analyzing AI data-Data Governance. Experience with data quality, governance, and handling sensitive data (PII)Automation & Scale. Build automated, scalable data pipelines and workflows. Preferred Qualifications:- Experience with AI/ ML systems, particularly in evaluation, monitoring, or observability- Familiarity with LLM applications and challenges in measuring generative AI quality- Experience building analytics for customer-facing products or Saas applications- Knowledge of data visualization tools (e.g., Power. BI, Streamlit, Snowflake)- Experience working with product analytics or user behavior data- Background in building self-service analytics or internal tooling- Understanding of legal, compliance, or regulated industry data requirements- Experience with real-time data processing and alerting systems#LI-TH 1 What’s in it For You?