Top Data Analyst Jobs in Mumbai

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

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.

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.

Collects, organizes, and analyzes large datasets to identify trends, correlations, and business opportunities. Maintains databases, spreadsheets, and data systems while ensuring data accuracy and integrity. Produces reports, MIS outputs, presentations, visualizations, and actionable recommendations for technical and non-technical stakeholders. Collaborates cross-functionally, supports facility-service operations, and monitors cleanliness scores, SLA compliance, and occupant complaint response times.

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.

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.

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.

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

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.

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

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.

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.

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.

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.

Consolidate and analyze Salesforce, partner, and internal data to produce dashboards, sales funnel analytics, and actionable insights. Support partner onboarding, performance management, forecasting accuracy, and cross-functional data-driven decision making.

Supports client operational and analytical processes by executing procedures, validating deliverables, identifying process improvements, coordinating stakeholders, testing updated workflows, documenting operational models, researching issues, and investigating client questions. Collaborates with senior analysts, delivery leads, engineering teams, and internal stakeholders to provide accurate, timely client results across multiple tasks and time zones.

Supports client operations through data analysis, process execution, quality validation, reporting, secondary research, and investigation of client questions. The role coordinates stakeholders across time zones, documents and tests operational procedures, identifies process improvements, and collaborates with engineering and delivery teams to ensure accurate, timely client deliverables.

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 analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Support data integrations and quality by checking ingested/system-generated data for anomalies, building and running QA checks and automations with Python and SQL, assisting on customer data queries and dashboards, and documenting findings to improve prediction reliability.

Build and maintain Python data pipelines to ingest, transform, validate, and load financial datasets. Onboard and document new vendor datasets, monitor pipelines, resolve data quality and delivery issues, and collaborate with researchers, analysts, and engineers to ensure reliable data for research and trading.

Join the Global Pricing Team to analyze market and customer data, build automated pricing dashboards and tools, apply statistical and ML methods (elasticity, regression, forecasting) to optimize pricing, and translate insights into commercial recommendations while driving process improvements and stakeholder enablement.