Top Data Analyst Jobs in Bengaluru

The Data Analyst will manage and analyze large financial datasets, design data quality frameworks, perform data validation, and deliver insights through dashboards and reports while collaborating with various stakeholders.

Owner of company-wide reporting and analytics for Growth, Marketing, Product, Finance and Operations. Build and maintain dashboards, automated reports and data pipelines (dbt), ensure data quality and governance, run growth and product analyses (CAC, ROAS, LTV, retention), support A/B testing and ad-hoc decision support, leverage AI for automation, and coordinate VAs on data tasks.

Design and analyze A/B tests, segment customers, measure campaign performance, build dashboards, and use SFMC/MCP, Google Analytics, SQL, and Excel to drive personalization and marketing recommendations.

Develop and maintain pricing and profitability models, forecast margins, analyze price sensitivity/elasticity and promotional impact, present insights to stakeholders, implement pricing changes, and lead pricing projects. Use marketing mix modelling to assess marketing impact and optimize spend.

Analyze retail data using Python, SQL, and AWS Redshift; develop ML models and Streamlit dashboards; communicate insights to clients; solve problems and support analytics delivery in-office in Bangalore.

Perform data extraction, cleaning, validation, analysis, and visualization to support business decisions. Build and maintain dashboards and reports, apply statistical techniques to identify trends, assist with predictive models, and present insights to stakeholders. Collaborate with cross-functional teams and support pharma/healthcare analytics and regulatory data standards when applicable.

Perform complex data analysis on large datasets using Python and PySpark; build and maintain scalable data pipelines and ETL processes; create reports and visualizations; collaborate with cross-functional teams to deliver data-driven insights; assist with predictive modeling and automate data tasks.

Analyze customer behavior and campaign performance to generate insights, build scalable data pipelines and automated analytics using SQL, Python, and PySpark, support personalized campaign execution, and collaborate with marketing stakeholders to drive revenue and improve customer experience.

Analyze customer behavior and campaign performance to generate actionable insights. Build and maintain scalable data pipelines and automated analytics workflows using SQL, Python, and PySpark. Support campaign planning, audience segmentation, execution tracking, and stakeholder communication to optimize engagement and ROI.

Lead analytics for loyalty and agri-tech: design dashboards, build ETL pipelines, perform segmentation and predictive modeling, ensure data quality and governance, and mentor junior analysts while partnering with product, marketing, sales and ops.

Analyze supply chain data using statistical methods and Big Data concepts; apply SCM and order-management knowledge (lead time, min/max, range-based planning); use CRM systems (SAP/Salesforce) and MS Office tools; build process maps, draw conclusions, make recommendations, prepare and present PPTs; short-term travel and immediate/local joiner required.

Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.

The Portfolio Data Analyst supports client onboarding and analyzes financial data, collaborating with various teams to enhance data solutions.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Lead end-to-end analytics by building and optimizing SQL data models, dashboards, and KPI frameworks. Perform deep-dive analyses on revenue, customer behavior, and operations, deliver actionable insights, and partner with product, growth, and leadership to drive data-informed decisions while ensuring data accuracy and reporting reliability.

The Forward-Deployed Data Analyst will engage with African Ministries of Health, build data management solutions, develop dashboards, and support public health decision-making.

The Senior Web3 Data Analyst will manage web3 analytics, monitor on-chain activity, analyze competitor metrics, and deliver insights for product and leadership teams.

Design, implement, and analyze marketing campaigns using Python and SQL. Extract and manage data, build performance reports and visualizations, provide actionable insights to optimize campaigns, collaborate with cross-functional teams, and communicate findings to clients. Leverage AWS services for data processing and reporting.

Design, develop, and automate BI reports, dashboards, and data models for financial and regulatory decision-making. Own RBI and statutory compliance reporting, optimize complex SQL queries, ensure data quality and governance, and collaborate with Finance, Risk, Compliance, and Tech teams. Drive process automation and explore AI/ML enhancements to improve reporting, anomaly detection, and analytics.

Maintain and operationalize the companys data governance artifacts: author and version taxonomies, business glossary terms, and data rules; detect and resolve rule conflicts and gaps; manage metadata and change history; support intake and certification of reference data; produce documentation and governance reporting to ensure rules are usable in production classification and quality workflows.

Lead end-to-end data analysis: transform raw data, develop reports and dashboards (PowerBI), monitor performance and quality, implement ETL and data modeling, apply predictive modeling/NLP/machine learning, and support cross-functional data-driven decisions.

The role involves supporting and maintaining the Azure Data Platform by managing incidents, monitoring performance, and optimizing database operations. Responsibilities include troubleshooting ADF, Synapse Analytics, and ensuring data security compliance.