Top Data Analyst Jobs in Kolkata

Lead the data solutions strategy for PR&C PSP division, managing data initiatives, teams, and collaborating on analytics projects. Ensure compliance, governance, and optimize data services while communicating technical solutions to stakeholders.

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

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

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.

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.

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 Senior Analyst will own Pricing Tools, manage data governance, drive pricing strategy decisions, execute audits, and enhance operational effectiveness.

Design, develop, and support analytics solutions including dashboards, reports, and data models. Clean and transform data, ensure data quality and governance, partner with stakeholders to gather requirements, apply AI prompting/tools to accelerate delivery, and implement process and change improvements for scalable, production-ready analytics.

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.

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

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.

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.

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.

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.