# Senior Lead Analyst - Data Management and Modelling at Thomson Reuters

Thomson Reuters is hiring a Senior Lead Analyst - Data Management and Modelling in Bengaluru, India. Level rates it Little AI ●○○○; you can [apply on Thomson Reuters](https://jobsbylevel.com/go/157b32a9-d75e-4dee-b6a3-287b2cf55723?utm_medium=ai-agent).

AI Level 1, AI centrality 14 out of 100. India, Bengaluru, Karnataka.

## Details

- Company: [Thomson Reuters](https://jobsbylevel.com/companies/thomson-reuters)
- AI level: AI Level 1 (score 14 out of 100)
- Location: India, Bengaluru, Karnataka
- Posted: October 5, 2026
- Apply: https://jobsbylevel.com/go/157b32a9-d75e-4dee-b6a3-287b2cf55723?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-lead-analyst-data-management-and-modelling-at-thomson-reuters-3f9882

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-lead-analyst-data-management-and-modelling-at-thomson-reuters-3f9882

## Description

Job Description: Our Data & Analytics team builds trusted, scalable, AI-ready data platforms that help people make decisions across our global organization. We're modernizing reporting, analytics, data management and AI with modern cloud technologies, semantic models and intelligent data products. As Senior Lead Analyst, Data Management & Data Engineering, you'll play a key part in delivering our Finance Systems modernization strategy and our Data Lake/Data Warehouse roadmap. You'll design and build scalable data foundations, modern data pipelines, governed semantic models and AI-enabled analytics. Your work will help the business get insights faster and make self-service analytics easier. Key Responsibilities: Design, develop and maintain scalable data pipelines, ingestion frameworks and transformation processes across Finance systems. Build modern ELT/ETL solutions using Microsoft Fabric, Snowflake, dbt and cloud-native technologies. Develop and improve data models that support enterprise reporting, analytics, AI and self-service. Make sure Finance data assets are scalable, reusable, governed and ready for AI. Lead the move of legacy reporting solutions onto cloud-based analytics platforms. Design conceptual, logical and physical data models using dimensional modeling methods such as Star Schema and Snowflake Schema. Develop enterprise Finance data models for FP&A, Revenue, Expense, Workforce and Financial Reporting. Help build semantic models with consistent business definitions, metrics, hierarchies and calculations. Work with business stakeholders to turn reporting and analytics needs into scalable data structures. Build AI-ready data foundations for Generative AI, Agentic AI, Conversational Analytics and Machine Learning. Work with analytics and AI teams to add Large Language Model (LLM) capabilities to Finance reporting. Help deliver AI-powered insights, data discovery and natural language queries. Use AI-assisted development practices to improve engineering productivity and data quality. Put data governance standards into practice, including metadata management, lineage, cataloging and security controls. Set up monitoring and validation processes to keep data accurate and reliable. Support master data and reference data management. Make sure work meets enterprise data policies and audit requirements. Gather business requirements and document data flows, processes and technical specifications. Work closely with Finance, Technology, Product and Analytics teams to deliver solutions. Lead cross-functional discussions and turn business problems into technical solutions. Spot issues early, suggest improvements and coordinate fixes with stakeholders. Mentor junior team members and promote engineering best practices. Required Qualifications: Strong experience with Microsoft Fabric, Power BI, Snowflake and dbt. Advanced SQL and data analysis skills Experience designing and building scalable data pipelines and transformation workflows Strong understanding of data warehousing, data lakes, Lakehouse architectures and dimensional modeling Hands-on experience with semantic models, enterprise reporting datasets and analytics platforms Skill in Python, Spark or other modern data engineering tools Experience integrating data from ERP or operational systems Strong analytical and problem-solving skills Excellent spoken and written communication Experience working directly with business stakeholders and turning their needs into technical solutions Ability to handle several priorities in a fast-paced environment Close attention to detail and a focus on delivering business value. Preferred Qualifications: Experience with Generative AI, Agentic AI frameworks, AI-powered analytics or conversational intelligence platforms Knowledge of Microsoft Copilot, Azure AI Services, OpenAI or enterprise AI ecosystems Experience with CI/CD pipelines, DevOps practices and Infrastructure as Code Familiarity with Databricks, Azure Data Factory, Synapse or

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