Top Data Analyst Jobs in Mumbai

Support centralized reference data for ESG, funds, corporate, and instrument analytics: troubleshoot data issues, define business rules and alerts, onboard products/consumers, profile datasets, collaborate with vendors and technology teams, and maintain SOPs and incident inventory to ensure high data quality.

Lead SOX compliance and ICFR activities including internal controls design, risk assessment, audit coordination, governance reporting, operational effectiveness, and stakeholder communication.

As a Data Analyst, you'll analyze complex data sets using statistical techniques, collaborate with SMEs, and prepare reports for stakeholders.

Operate and maintain data pipelines that validate, transform and enrich policyholder data for actuarial modelling. Investigate and resolve data issues, implement controls and remediation, produce reports and dashboards, maintain documentation and governance evidence, and support audits and stakeholders. Deliver BAU updates and contribute to continuous improvement of data processes.

Translate operational and commercial data into trusted Power BI dashboards and semantic models for Hub Operations. Gather requirements, design data models and DAX measures, build production-grade reports with RLS, manage workspace governance and deployment pipelines, collaborate with central data teams, and mentor junior analysts while ensuring maritime operational relevance.

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.

Validate, maintain, and onboard equity and cross-asset research data for index construction. Implement data validation checks, perform monthly and historical data QA, run UAT for pipelines, build data dictionaries and dashboards, automate processes, and collaborate with vendors, engineering, and research to resolve data issues.

Execute end-to-end data quality lifecycle for Corporate Finance & Treasury/Chief Investment Office items: intake, prioritization, root-cause analysis, remediation tracking, validations, SLA monitoring, governance reporting, stakeholder coordination, and contributing to training and continuous improvement.

The Data Analyst will analyze data, develop dashboards, conduct statistical analyses, and collaborate with teams to provide insights that drive business decisions.

Design, build, and maintain Power BI dashboards and reusable tabular data models; administer Power BI Service; ingest and transform data using SQL/Python and ETL tools; support stakeholders with ad-hoc reporting, documentation, demos, and Power Platform requests.

Support client operations by executing and improving predefined operational processes, performing data mining (SQL), quality checks, coordinating stakeholders, assisting engineering with procedure changes, and delivering analytic results to clients.

No Data Analyst responsibilities are described. The posting instructs candidates to register on the JobDials portal, post their CV, and follow the company on social media. This is a recruitment/registration notice rather than a role description.

Lead complex analytics for global complaint vigilance: perform trend and risk analyses, build dashboards, support data ingestion and data-lake strategy, present findings to QA and cross-functional partners, and enable corrective action and regulatory responses.

Label and validate datasets (documents, images, video, audio) for document fraud, deepfake, PAD, and face recognition. Create bounding boxes, landmarks, time-based frame labels, and flag tampering. Document edge cases, review peer annotations, ensure quality standards, and collaborate with ML engineers to improve datasets and tools.

The role involves data analytics with a focus on querying large data sets, visualization using Tableau, and applying quantitative skills, predominantly in the FMCG and Food & Distribution sectors.

The Data Analyst prepares, analyzes, and validates data, applying statistical methods and visualization techniques to derive insights and support business objectives. Communicates findings effectively and collaborates with Data Engineers for model and dashboard implementation.

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.

The Data Analyst will analyze data to provide insights for decision-making across pricing, go-to-market strategy, and revenue performance, collaborating with stakeholders.

The Data Analyst will support product marketing initiatives by analyzing data, tracking KPI progress, developing dashboards, and providing actionable insights to inform decision-making across business units.

Gather, clean, and analyze data from multiple sources; build and maintain Power BI and Excel dashboards; run SQL queries and Python scripts for automation; collaborate with cross-functional teams to interpret trends and communicate insights; create documentation and improve reporting processes.

Combine customer marketing and data analysis to drive engagement and retention. Collect and clean data from CRM, web, and campaign tools; build dashboards and segments; run email, loyalty and upsell campaigns; measure ROI and retention; design sales incentive schemes; support forecasting, territory planning, and sales-marketing alignment.

Perform advanced data analysis and statistical modeling in R, build and optimize complex SQL queries, ensure data accuracy and quality, collaborate with stakeholders to deliver insights, and optionally process large datasets using Hadoop or similar big-data technologies.

The Senior Business Analyst will analyze and resolve data quality issues, coordinate with data teams, document business requirements, and implement data governance frameworks to improve business productivity and data management processes.

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.

Perform exploratory analysis, build dashboards and reports (Tableau), write SQL queries, use Python for data cleaning and predictive models, collaborate with stakeholders, communicate insights, and maintain data models, pipelines, and quality checks.