Senior Lead Analysts - Data Management and Modeling

Overview

Lead a hands-on technical delivery of Data & AI solutions, from raw data through to AI-enabled decisions, covering medallion architecture, reusable data products, semantic models, agents and applications. This is a technical leadership role with mentoring responsibilities and no direct people management.

What you'll do

• Deliver production-ready Data & AI solutions, staying hands-on in investigation, architecture, coding, modelling, testing and implementation.
• Build AI-ready data foundations and agentic solutions grounded in structured data, governed measures, semantic metadata and enterprise knowledge.
• Build production-ready Agentic AI, LLM, RAG and tool/API orchestration patterns using enterprise engineering standards.
• Build analytical and AI-enabled applications using Fabric application capabilities, Rayfin, or comparable frameworks, integrated with semantic models, APIs, agents and enterprise security.
• Build robust 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.
• Integrate lakehouse, warehouse and semantic assets using batch, streaming, Direct Lake or API patterns.
• Deliver reusable, domain-aligned data products with clear ownership, data contracts, quality controls, documentation and consumption interfaces.
• Design dimensional models with explicit grain, conformed dimensions, facts, hierarchies, slowly changing dimensions, and bridge and relationship patterns.
• Build and optimize Fabric and Power BI semantic models with governed measures, DAX, relationships and metadata.
• Explore data, frame hypotheses, engineer features, and apply segmentation, forecasting, classification or anomaly detection where they support a business decision.
• Evaluate analytical and machine-learning approaches against suitable baselines using statistical reasoning, validation and business-relevant measures, and explain limitations.
• Collaborate with data scientists on deeper modelling needs.
• Lead end-to-end technical delivery: define scope, estimate and plan work, manage dependencies and risks, coordinate stakeholders, and own release readiness, adoption and operational handover.
• Mentor engineers through architecture, design and code reviews, debugging, testing and production-readiness practices.
• Build data quality, observability, automated testing, lineage, security, privacy, responsible AI, version control and CI/CD into every stage of delivery.
• Turn successful solutions into reusable data products, semantic assets, agent capabilities and engineering accelerators.
• Start with the business decision or workflow, and co-shape outcomes, success measures, data needs and adoption with stakeholders.
• Move from discovery to prototype to production, validating assumptions with real enterprise data and early feedback.
• Measure success by business value, trusted metrics, adoption, engineering quality and reuse.
• Solve immediate needs while strengthening reusable enterprise foundations.

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.

Nice to have

• Experience with dbt, Apache Airflow, Git/GitHub and automated deployment frameworks.
• Broader Power BI and Fabric experience across OneLake, Lakehouse, Warehouse, Notebooks, Direct Lake and application capabilities.
• Experience with AI solutions grounded in semantic models, metadata, knowledge graphs or governed enterprise data.
• Experience with agentic frameworks, model evaluation, observability and responsible AI practices.
• Exposure to machine-learning experimentation, feature pipelines and operationalizing lightweight predictive solutions.
• Knowledge of governance, privacy, security, lineage and enterprise architecture.
• Experience delivering reusable data products and platforms in Agile or iterative models.
• Experience designing data contracts and consumption interfaces that enable reuse across analytics, applications and AI solutions.
• Experience integrating lakehouse, warehouse and semantic assets through batch, streaming, Direct Lake or API-based patterns, with end-to-end lineage and auditability.
• Experience optimizing dimensional and semantic models with conformed dimensions, hierarchies, bridge tables and complex relationships.
• Experience evaluating analytical and AI-enabled solutions against suitable baselines and business-relevant measures, communicating limitations and incorporating stakeholder feedback.
• Experience partnering with business and product stakeholders to take ambiguous needs from discovery and prototyping through production adoption and operational handover.
• Experience creating reusable engineering accelerators and guiding engineers through architecture reviews, debugging and production-readiness practices.

Details

• Location: Bangalore, Karnataka, India (Bengaluru, Karnataka).
• Hybrid work model for office-based roles.