Senior Lead Analyst – Data Management, Modeling

Thomson Reuters

. Build trusted data and semantic foundations for scalable enterprise AI capabilities .

Posted 11/4/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building scalable AI-enabled applications and data products, leveraging strong skills in SQL, Python, and data engineering methodologies. Proven ability to lead technical delivery, mentor engineers, and collaborate with stakeholders to drive data-driven decision-making.

Highest-signal resume keywords

Data EngineeringAI EngineeringSQLPythonDimensional Modelling

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Hard Skills

Data EngineeringAI EngineeringSQLPythonDimensional ModellingStatistical ReasoningFeature EngineeringAutomated TestingVersion ControlData Quality

Soft Skills

Clear CommunicationMentoringCollaborationProblem-SolvingLeadership

Tools & Technologies

SnowflakeMicrosoft FabricPower BIDAXApache AirflowDbtRayfinAgentic AILLMsRAG

Industry Keywords

Data ProductsSemantic ModelsMedallion ArchitectureGoverned MeasuresExploratory AnalysisMachine LearningPredictive MethodsData ContractsOperational SupportAgile Delivery

Tech Stack

Tools & technologies

AirflowApacheETLPythonSnowflakeSQL

About the role

Key responsibilities & impact

Build trusted data and semantic foundations for scalable enterprise AI capabilities
Own delivery from raw data to AI-enabled decisions across medallion architecture, reusable data products, semantic models, agents, and applications
Partner with business, product, Data & Analytics, and technology stakeholders to define outcomes, success measures, and data needs
Turn ambiguous challenges into technical designs, prototypes, and production solutions, remaining involved through adoption and improvement
Deliver production-ready Data & AI solutions through investigation, architecture, coding, modelling, testing, and implementation
Build AI-ready data foundations and agentic solutions using structured data, governed measures, semantic metadata, and enterprise knowledge
Build Agentic AI, LLM, RAG, and tool/API orchestration patterns
Build analytical and AI-enabled applications using Fabric application capabilities, Rayfin, or comparable frameworks
Build and optimize ELT/ETL pipelines using Snowflake, Microsoft Fabric, SQL, Python, dbt, Apache Airflow, or comparable technologies
Implement bronze, silver, and gold medallion layers with lineage and auditability
Deliver reusable, domain-aligned data products with ownership, data contracts, quality controls, documentation, and consumption interfaces
Design dimensional models and optimize Fabric and Power BI semantic models with governed measures, DAX, relationships, and metadata
Explore data, engineer features, and apply segmentation, forecasting, classification, or anomaly detection to support business decisions
Evaluate analytical and machine-learning approaches using statistical reasoning, validation, baselines, and business-relevant measures
Collaborate with data scientists on deeper modelling needs
Lead end-to-end technical delivery, including scope, estimates, planning, dependencies, risks, release readiness, adoption, and operational handover
Mentor engineers through architecture, design, code reviews, debugging, testing, and production-readiness practices
Embed data quality, observability, automated testing, lineage, security, privacy, responsible AI, version control, and CI/CD
Turn successful solutions into reusable data products, semantic assets, agent capabilities, and engineering accelerators
Measure success by business value, trusted metrics, adoption, engineering quality, and reuse

Requirements

What you’ll need

8+ years of experience in data engineering, analytics engineering, AI engineering, or closely related technical roles
Hands-on experience with Agentic AI, LLMs, agents, RAG, and tool/API integration or orchestration
Experience building data-driven or AI-enabled applications using Fabric, Rayfin, or comparable frameworks
Experience with Snowflake, Microsoft Fabric, or comparable analytical platforms, including lakehouse and warehouse patterns
Strong SQL and Python skills, with experience delivering production-grade data solutions
Experience delivering data products through bronze, silver, and gold layers, with quality controls, lineage, and clear consumption interfaces
Strong dimensional modelling skills, including grain, facts, dimensions, star and snowflake schemas, slowly changing dimensions, and many-to-many patterns
Proven enterprise semantic modelling experience, including governed business definitions and reusable measures
Proficiency in Power BI, DAX, and performance optimization, or equivalent technologies
Working knowledge of exploratory analysis, statistical reasoning, feature engineering, and practical predictive methods such as forecasting, classification, or anomaly detection
Ability to frame hypotheses, select baselines and evaluation measures, validate results, communicate uncertainty, and work with data scientists on modelling approaches
Experience with orchestration, automated testing, version control, CI/CD, data quality, and operational support
Ability to lead technical workstreams as a hands-on senior individual contributor and mentor engineers through design and code reviews
Experience managing scope, estimates, dependencies, risks, releases, and operational handover, taking solutions from concept through adoption
Clear communication with technical, business, and senior stakeholder audiences
Preferred: experience with dbt, Apache Airflow, Git/GitHub, automated deployment frameworks, OneLake, Lakehouse, Warehouse, Notebooks, Direct Lake, application capabilities, semantic models, metadata, knowledge graphs, governed enterprise data, agentic frameworks, model evaluation, observability, responsible AI, machine-learning experimentation, feature pipelines, governance, privacy, security, lineage, enterprise architecture, Agile or iterative delivery, data contracts, consumption interfaces, and reusable engineering accelerators

Benefits

Comp & perks

Flexible hybrid working environment for office-based roles
Work from anywhere for up to 8 weeks per year
Flexible vacation
Two company-wide Mental Health Days off
Access to the Headspace app
Retirement savings
Tuition reimbursement
Employee incentive programs
Resources for mental, physical, and financial wellbeing
Two paid volunteer days off annually
Opportunities to get involved with pro-bono consulting projects and ESG initiatives
Career development, continuous learning, and skill development through Grow My Way programming
Flexible work arrangements supporting work-life balance