Top Data Analyst Jobs in Bangalore

Perform product and business analytics: translate open-ended questions into hypotheses, analyze funnels and retention, run experiments, build dashboards and pipelines, collaborate with engineering and product, and deliver AI-enabled analytics to drive measurable business outcomes.

Analyze product, engineering, delivery, and commercial data to define KPIs, identify trends, and support evidence-based decisions. Build Power BI dashboards from product telemetry to measure adoption, engagement, feature usage, and customer behavior. Improve data quality, automate recurring reporting, and communicate actionable insights to technical and non-technical stakeholders.

Conduct secondary research on private-market companies, M&A transactions, investments, ownership structures, valuations, and corporate events. Build and validate structured intelligence, resolve conflicting or incomplete information, identify new companies and relationships, and document evidence-backed findings. Use AI-assisted research tools to improve productivity and collaborate with global data, product, and engineering teams to address data quality issues and coverage gaps.

Monitor and improve risk data quality by validating processing, reviewing day-over-day anomalies, investigating exceptions, and coordinating remediation with upstream teams and data consumers. Maintain validation lineage, refine rules, support data governance, and communicate data availability. Partner with finance, risk, technology, and business teams to define requirements, test changes, and improve data processes and user experiences.

Leads analytics for a mobile gaming business, owning investigations into funnels, retention, churn, player value, monetization, game economy, and experimentation. Builds churn prediction and forecasting models, designs and analyzes A/B tests, architects data pipelines, ensures data quality, creates self-service dashboards, communicates recommendations to leadership, and mentors analysts. Requires strong SQL, Python, statistical reasoning, visualization, and experience working with large-scale data systems.

Analyze and interpret large healthcare datasets, build and maintain data pipelines and structures, collaborate with cross-functional teams to deliver actionable insights, and document data processes and analyses.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Own the company’s data foundations and self-serve analytics strategy. Design and maintain event-based data models, scalable datasets, dashboards, monitoring, and alerting systems. Build and validate analytics solutions from raw pipelines through executive dashboards, improve data reliability, and drive adoption across teams. Identify opportunities to integrate AI into analytics workflows and translate technical concepts into actionable insights for non-technical stakeholders.

Lead the analytics function by developing dashboards, reports, data models, forecasts, and predictive models. Partner with cross-functional leaders to translate business challenges into actionable insights, ensure data accuracy, and support decisions across customer experience, sales, marketing, operations, and finance. Manage data pipelines and BI tool governance while mentoring analysts and junior data scientists, defining the analytics roadmap, and prioritizing team workloads.

Manages operational data, develops recurring MIS reports and dashboards, analyzes performance metrics, ensures data integrity, and automates reporting workflows. The role extracts and transforms data from multiple systems, identifies trends and optimization opportunities, collaborates with Operations, IT, and Finance, prepares executive summaries, and supports operational software and database upgrades.

Analyze sales performance, customer behavior, lead conversion, and churn data to identify trends and revenue opportunities. Build and maintain dashboards and reports, improve data visualization and reporting automation, and submit data reports to partners. The role supports sales decision-making through accurate, scalable, and actionable insights.

Maintain and govern enterprise master data by defining standards, integrating systems, supporting SAP MDM/MDG users, translating business needs into technical designs, training users, coordinating consultants, and improving data lifecycle and system configurations.

Maintain accuracy, governance, and lifecycle of enterprise master data in SAP environments. Define data standards, map system integrations, support global SAP MDG/MDM users, collaborate with Supply Chain/Finance/IT, design technical solutions, provide training, coordinate consultants, and ensure data quality and system controls.

Process and analyze large-scale datasets using PySpark/Python and AWS data services; build and maintain scalable ETL pipelines, validate data quality, and translate findings into actionable insights. Collaborate with engineers, GIS, BI, and commercial teams; support KPI tracking, performance reviews, and AI-enabled analytics workflows.

Leads enterprise data engineering, analytics, reporting, regulatory data, and platform initiatives for banking and financial services. Designs data warehouses, lakes, pipelines, integrations, reporting marts, dashboards, and Oracle solutions. Ensures data quality, governance, reconciliation, lineage, and regulatory compliance across lending, risk, securities, tax, payments, treasury, and customer data. Provides L2/L3 production support, resolves incidents, drives performance improvements, and partners with senior stakeholders and global teams.

Leads enterprise data engineering, warehousing, integration, analytics, reporting, and regulatory data initiatives for banking and financial services. Designs scalable pipelines and data platforms, develops Oracle SQL and PL/SQL solutions, builds BI dashboards, manages data quality and governance, supports lending, risk, securities, tax, payments, treasury, and customer data domains, and provides L2/L3 production support. Partners with senior stakeholders and leads global teams to improve reporting accuracy, compliance, reconciliation, and operational performance.

Perform gap analyses across trading data from multiple source systems, identify discrepancies, build automated reports using Python and SQL, create visualizations (Power BI or Python libraries), present findings to stakeholders, and coordinate with upstream/downstream teams to implement logic and document requirements.

Lead and manage a team of data analysts, oversee analytics projects, engage stakeholders, and drive automation initiatives to enhance efficiency in healthcare marketing research.

Lead data strategy and governance for Coupang’s Retail and Selection team. Architect ETL pipelines, improve data integrity, and use SQL, programming, LLMs, and prompt engineering to automate catalog and vendor-data analysis. Build decision models, simulations, dashboards, and knowledge-management systems supporting selection, pricing, and inventory decisions. Translate findings for executives, train business users, and mentor analysts.

Design, develop, and support analytics solutions, dashboards, reports, and data models. Prepare and transform data, ensure data quality and governance, collaborate with stakeholders, leverage AI to accelerate delivery, and drive process improvements to enable data-driven decisions.

Support RegTech data-quality operations: develop and maintain Collibra DQ rules, run reconciliations, investigate data issues, validate changes, automate checks with Python, analyze large datasets using SQL and Snowflake, document procedures, gather data requirements, and produce regular data-quality reporting.

Design and build customer-facing Power BI dashboards, develop and track KPIs, perform advanced SQL-driven analysis and data mining, partner with Customer Success and Data Science to standardize value-engineering frameworks, and translate data into actionable insights that demonstrate measurable customer ROI.

Perform data analysis to generate insights and support decision-making. Design and execute analysis projects, collaborate with cross-functional teams, implement best practices, troubleshoot issues, document processes, and ensure data accuracy and reliability.

Analyze large financial and operational datasets, build and maintain dashboards and reports, write efficient SQL queries, perform deep-dive analyses, automate workflows with Python, ensure data quality, and partner with Finance, Product, Operations, and Data Engineering to surface actionable business insights.

Design, build, and maintain cross-vendor market data pipelines and QA processes. Investigate complex data issues, handle escalations, collaborate with product and engineering via JIRA, and ensure data accuracy for equities fundamentals, estimates, and market data.