Research Engineer – Data & Analytics, ML-Enabled Systems

Thomson Reuters

. 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.

Posted 4/21/2026full-timeEagan • Minnesota, Missouri, New York, Texas • 🇺🇸 United StatesSeniorLead💰 $127,400 - $236,600 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudETLPythonSQL

About the role

Key responsibilities & impact

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).

Requirements

What you’ll need

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, PowerBI, 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

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work hours
Paid time off
Remote work options
Flexible vacation
Company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Mental, physical, and financial wellbeing resources

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLdata engineeringanalytics engineeringETLELTdata warehousingdata modelingdata governancedata quality

Soft Skills

communicationcollaborationself-drivenproject management

Certifications

Bachelor's degreeMaster's degree