Data Analyst – Analytics Engineering & AI

Overview

Support the development of modern enterprise analytics capabilities and help accelerate digital analytics toward AI-supported insights. Work across business analytics, data visualization, analytics engineering, data modeling, and AI-enabled analytics to transform business questions into trusted datasets, analytical models, dashboards, reports, and actionable insights.

What you'll do

• Partner with business stakeholders and senior analytics team members to understand business questions, reporting requirements, KPIs, and analytical needs.
• Analyze large and complex datasets to identify trends, patterns, opportunities, anomalies, and drivers of business performance.
• Develop recurring and ad hoc analyses supporting commercial, customer, digital, operational, and strategic initiatives.
• Build and maintain dashboards, reports, scorecards, and visualizations.
• Write and maintain SQL queries and data transformations used to create analytical datasets and support reporting and analysis.
• Assist in developing reusable analytical datasets and data models.
• Support a trusted semantic analytics layer, including business definitions, KPIs, dimensions, measures, hierarchies, and relationships.
• Document and validate metric definitions and ensure consistent interpretation across analytical products.
• Perform data profiling, validation, reconciliation, and quality checks.
• Investigate data-quality and reporting issues and identify root causes with analytics engineering and data engineering teams.
• Maintain documentation covering data sources, transformations, metric calculations, business rules, assumptions, and analytical logic.
• Assist with ETL/ELT and analytics engineering activities, including data transformations, testing, validation, and maintenance of curated analytical datasets.
• Use Python or similar analytical technologies for data preparation, automation, exploratory analysis, validation, and analytical workflows.
• Support customer, product, digital, marketing, commercial, and operational analytics initiatives.
• Assist with experimentation, segmentation, funnel analysis, customer journey analysis, KPI tracking, forecasting, and other analytical methodologies.
• Identify opportunities to automate manual reports, repetitive analysis, and data-preparation processes.
• Support self-service, proactive, and AI-enabled analytical experiences.
• Participate in developing and testing conversational analytics, natural-language querying, AI-generated insights, semantic search, and other emerging analytical capabilities.
• Validate AI-generated analytical outputs against trusted datasets, established metrics, source systems, and documented business rules.
• Organize metadata, business definitions, and semantic context for AI-enabled analytics solutions.
• Collaborate with data engineering, architecture, security, product, and governance teams to follow organizational standards.
• Participate in peer reviews, testing, documentation, and continuous improvement.
• Communicate analytical findings to technical and non-technical stakeholders using visualizations, summaries, and recommendations.
• Develop knowledge of modern analytics, data engineering, semantic technologies, cloud data platforms, generative AI, and emerging analytical practices.
• Support reusable business metrics and KPIs; document business terminology and map business concepts to enterprise data.
• Assist in defining relationships between customers, products, channels, transactions, campaigns, digital interactions, and other business entities.
• Build analytical datasets reusable across dashboards, reports, analyses, and AI-enabled applications.
• Identify differences between business terminology and source-system definitions.
• Test natural-language questions against analytical datasets and identify inaccurate or ambiguous AI-generated responses.
• Support data and semantic quality controls that improve accuracy, consistency, explainability, and trust.
• Develop an understanding of how governed enterprise data can support AI agents and conversational analytical experiences.

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.
• Equivalent combination of education and relevant professional experience may be considered.
• 2–5 years of experience in data analytics, business intelligence, analytics engineering, data engineering, data science, or a related discipline.
• Experience working with structured datasets and translating business questions into analytical outputs.
• Experience developing dashboards, reports, analyses, or analytical datasets in a business environment.
• Experience working with relational databases, cloud data warehouses, or enterprise analytical platforms.
• Strong SQL skills, including joins, aggregations, subqueries, common table expressions, window functions, and analytical querying.
• Working knowledge of Python or another analytical programming language for data manipulation, automation, analysis, or validation.
• Experience with visualization and business intelligence technologies such as Tableau, Power BI, or similar tools.
• Understanding of data structures, relational databases, analytical datasets, and basic data modeling concepts.
• Familiarity with ETL/ELT concepts and modern data-transformation workflows.
• Understanding of data-quality principles, including validation, reconciliation, completeness, consistency, and accuracy.
• Basic understanding of cloud data architectures and modern data warehouse technologies.
• Ability to understand how business definitions, metadata, data relationships, and metric calculations affect analytics and AI-generated insights.
• Strong analytical and problem-solving skills.
• Curiosity and willingness to investigate unfamiliar data and business problems.
• Ability to translate data into clear observations and business insights.
• Strong attention to detail and commitment to analytical accuracy.
• Ability to communicate findings effectively to technical and non-technical audiences.
• Ability to work collaboratively across analytics, engineering, product, technology, and business teams.
• Ability to manage multiple assignments and priorities in a fast-paced environment.
• Willingness to ask questions, challenge assumptions constructively, and seek deeper understanding of business problems.
• Strong documentation and organizational skills.
• Commitment to continuous learning and development across analytics, data engineering, and AI technologies.

Nice to have

• Advanced degree or relevant professional certifications.
• Exposure to data modeling, dimensional modeling, semantic models, metrics layers, or curated analytical datasets.
• Experience with cloud-based data platforms such as Snowflake, Teradata, Hadoop, AWS, Azure, Google Cloud Platform, or similar technologies.
• Experience with digital analytics technologies such as Google Analytics or Adobe Analytics.
• Exposure to enterprise systems such as SAP, ERP, CRM, digital commerce, marketing, customer, or operational platforms.
• Exposure to generative AI, conversational analytics, natural-language querying, semantic search, machine learning, or AI-enabled analytics.
• Familiarity with version control, documentation, testing, or collaborative software/data development practices.
• Interest in or exposure to generative AI, large language models, semantic search, embeddings, knowledge models, or AI agents.

Details

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