Top Data Analyst Jobs in Mumbai

Ensure accuracy, completeness, and integrity of Salesforce customer and account data. Build automated validation and enrichment pipelines (SQL/Python), perform large-scale cleansing and deduplication (DemandTools, Data Loader), create monitoring dashboards (Power BI), enforce governance, and apply AI/LLMs to improve matching, classification, and anomaly detection so data never blocks Quote-to-Cash processes.

Manage and operate data feeds and reference data for funds, indices, securities, and performance. Perform data ingestion, validation, research, reporting, operational controls, documentation, and stakeholder coordination to ensure accurate timely delivery and mitigate operational risk.

Lead insider risk and data loss investigations from triage to closure, operate and tune DLP across environments, build detections and workflows, partner with HR/Legal/Compliance/IT, mature insider risk program, support audits, and mentor others.

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.

Lead the Data Desk: triage and route L1/L2 requests, perform code reviews for junior analysts, drive automation and self-service dashboards, run daily operations and people development, and ensure data quality and SLA adherence.

Analyze data quality, apply data maintenance, and integrate publicly available data. Collaborate with the product manager on data requirements and perform segmentation analysis.

Ensure accuracy, completeness, and consistency of product data by validating, cleansing, enriching, profiling, and monitoring quality. Maintain data dictionaries, lineage, metadata, governance policies, dashboards, and compliance. Collaborate with stakeholders, operations, and other data stewards; train users and gather feedback to improve data products.

Ensure accuracy, completeness, and consistency of product data by validating, cleansing, profiling, and enriching content. Define data quality standards, maintain data dictionaries and lineage, implement governance policies, build quality dashboards, collaborate with stakeholders and operations, train users, and ensure compliance with privacy and regulatory requirements.