Senior Data Analyst – Analytics Engineering & AI

Overview

Serve as a senior individual contributor at the intersection of business analytics, analytics engineering, data engineering, semantic modeling, and AI-enabled analytics. Transform complex business questions into trusted data products, scalable analytical solutions, reusable semantic models, and actionable insights while helping create an AI-ready analytics foundation.

What you'll do

• Translate complex business questions into analytical requirements, data models, metrics, dashboards, data products, and actionable recommendations.
• Develop advanced analyses identifying trends, opportunities, root causes, customer behaviors, operational drivers, and performance-improvement areas.
• Design and maintain scalable analytical datasets, reusable data models, and a trusted semantic analytics layer.
• Standardize business entities, dimensions, measures, KPIs, relationships, definitions, and calculation logic across analytical applications.
• Partner with stakeholders and data owners to establish consistent enterprise metric definitions.
• Structure data, metadata, business definitions, lineage, relationships, and contextual information for AI and conversational analytics solutions.
• Support conversational analytics, natural-language querying, AI-generated insights, intelligent search, and agentic analytics workflows.
• Evaluate AI-generated analytical responses by validating calculations, semantic context, data mappings, source data, business rules, and outputs.
• Partner with data engineering teams on ETL/ELT pipelines, analytical transformations, curated datasets, and reusable data products.
• Develop complex SQL transformations and use Python or similar technologies to automate processes, perform analysis, validate data, and improve workflows.
• Perform data profiling, validation, reconciliation, and root-cause analysis to address data-quality issues.
• Develop dashboards, scorecards, visualizations, and self-service analytical products.
• Support experimentation, KPI measurement, customer journey analysis, forecasting, segmentation, attribution, and performance measurement.
• Collaborate with architecture, engineering, security, product, and governance teams on enterprise standards.
• Document analytical models, semantic definitions, transformations, business rules, lineage, assumptions, and metric calculations.
• Identify opportunities to simplify and automate reporting and analytical processes.
• Contribute to modernization from dashboard-centric reporting toward AI-supported, proactive, and conversational insights.
• Research and evaluate emerging analytics engineering, semantic technology, generative AI, machine learning, and modern data-platform capabilities.
• Communicate analytical findings, recommendations, limitations, and implications to business and technology stakeholders.
• Mentor analysts and team members on analytical methodologies, SQL, data modeling, semantic design, visualization, and modern analytics technologies.
• Define reusable enterprise metrics and KPI logic; model relationships among customers, products, channels, transactions, campaigns, digital interactions, and other business entities.
• Create analytical models consumable by humans, BI platforms, APIs, and AI applications.
• Translate business terminology into structured metadata and machine-understandable definitions, and resolve inconsistencies between source-system terminology and enterprise definitions.
• Design analytical context for natural-language querying and AI interpretation, test AI-generated answers against governed data, and help establish guardrails for data access, governance, privacy, security, and business rules.

What you'll need

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, Statistics, Mathematics, Economics, or a related quantitative discipline.
• 6+ years of experience in data analytics, business intelligence, analytics engineering, data engineering, data science, or a related discipline.
• Demonstrated experience translating ambiguous or complex business problems into structured analytical solutions.
• Strong experience with enterprise-scale data environments and large, complex datasets.
• Experience developing analytical data models and curated datasets for reporting, analytics, and downstream consumption.
• Experience with cloud-based data warehouses or analytical platforms such as Snowflake, Teradata, Hadoop, AWS, Azure, Google Cloud Platform, or similar technologies.
• Experience designing or working with semantic models, metrics layers, dimensional models, business metadata, or governed analytical datasets.
• Advanced SQL skills, including complex transformations, joins, window functions, optimization, reconciliation, and analytical querying.
• Proficiency with Python for analytics, data manipulation, automation, validation, or analytical application development.
• Strong understanding of data modeling, including dimensional modeling, fact/dimension structures, analytical datasets, and reusable business entities.
• Understanding of modern ETL/ELT and analytics engineering practices, including transformation pipelines, testing, documentation, version control, and deployment.
• Experience with visualization and business intelligence technologies such as Tableau, Power BI, or similar platforms.
• Understanding of data quality, data lineage, metadata management, governance, and master/reference data concepts.
• Knowledge of APIs, structured and semi-structured data, cloud data architectures, and modern data integration patterns.
• Understanding of how metadata, business terminology, semantic relationships, metric definitions, and governed data influence AI-generated analytical responses.
• Strong analytical and structured problem-solving skills.
• Ability to move between business problems and technical implementation.
• Strong curiosity and ability to uncover the business meaning behind data.
• Ability to communicate complex analytical and technical concepts clearly to technical and non-technical audiences.
• Strong stakeholder-management and consulting skills.
• Ability to independently manage multiple priorities and analytical initiatives.
• Strong attention to data quality, analytical accuracy, and business context.
• Ability to challenge assumptions constructively and use data to influence decisions.
• Collaborative approach across analytics, engineering, architecture, product, security, and business teams.
• Commitment to continuous learning and adoption of emerging analytics and AI technologies.

Nice to have

• Master's degree in a quantitative, technical, or business discipline.
• Experience supporting AI-enabled analytics, conversational analytics, natural-language-to-data experiences, generative AI applications, or semantic search.
• Experience with digital analytics platforms such as Google Analytics or Adobe Analytics.
• Experience integrating analytical data with enterprise systems such as SAP, ERP, CRM, digital commerce, marketing, customer, or operational systems.
• Familiarity with generative AI, large language models, retrieval-based architectures, semantic search, embeddings, knowledge models, or AI agents.

Details

• Location: Bangalore, India.
• Work schedule: Standard, Monday through Friday.
• Environmental conditions: Office.