Top Data Analyst Jobs in Chennai

Work with stakeholders to define data needs, source and merge datasets, run exploratory analysis and hypothesis testing, build predictive models, evaluate campaigns, and ensure analytics comply with data governance standards.

Supports liquidity regulatory reporting controls by documenting data flows, business data elements, lineage, transformations, and controls from booking systems to reporting platforms. Performs SQL-based data analysis, test-case development, functional and business testing, quality remediation, root-cause analysis, requirements management, stakeholder coordination, and management reporting. Contributes to implementation activities, change control, documentation standards, and training junior team members.

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.

Maintain and modernize a metadata-driven financial reporting data integration platform that ingests 200M+ rows/day using Oracle PL/SQL. Ensure production discipline (rerun safety, parity validation, ops handoff), validate data results, debug transformations, and support a potential migration to Databricks/PySpark. Collaborate with banking subject-matter experts and translate legacy dynamic SQL/COBOL logic to Python for future platform migration.

Gather and process data, conduct trend analyses, develop statistical models, and identify insights to support advertising and media planning efforts.

Supports business and enterprise analytics by collecting, cleaning, validating, analyzing, and presenting data. Builds reports and dashboards using SQL, Excel, Power BI, Tableau, or similar tools; performs trend analysis; identifies data-quality issues; documents processes and reporting logic; collaborates with stakeholders; and communicates actionable insights while following data governance and security standards.

Leads end-to-end data analysis, from problem definition and data sourcing through validated insights and recommendations. Builds complex datasets, models, dashboards, and analytical pipelines using advanced statistical methods, forecasting, segmentation, experimentation, and predictive analytics. Partners with business, engineering, product, and governance teams; presents findings to leadership; mentors junior analysts; and improves analytical standards, documentation, data quality, security, and governance.

Perform accurate updates and quality checks on mutual fund data, label and validate AI-extracted data, meet extraction targets, escalate defects, provide status updates, and generate project reports to support management.

Manage distributor data cases end-to-end, act as primary client contact, build and maintain data connections, support coverage and reporting, and source data from public providers to improve the data ecosystem.

Lead and deliver advanced analytics and machine learning solutions across Sales, Vendor Management, Cloud, eCommerce, Finance, and Supply Chain. Scope projects, build and deploy predictive models, produce executive-ready insights, and partner cross-functionally to drive revenue, operational efficiency, and strategic decisions.

Create and automate client-facing marketing dashboards and custom reports using Power BI, SQL, Excel/Google Sheets. Query ad and web platforms, interpret marketing metrics, reduce manual work, document BI processes, and support paid media and SEO teams.

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

Ensure reliability, scalability, and performance of the enterprise data platform. Automate IaC, CI/CD, and deployments; manage observability, governance, FinOps, and incident response; collaborate with engineering and analytics teams to improve platform reliability and developer experience.

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.

Support Data Scientists and Product Owners by preparing and analysing large datasets using tools like Databricks and Snowflake. Produce metrics (precision/recall), create datapreps, surface insights via dashboards, and communicate findings to stakeholders while following Agile practices.

Support Data scientists and product owners by preparing and analyzing large datasets, building dataprep pipelines, evaluating model metrics (precision/recall), creating dashboards and insights, and communicating findings to global stakeholders using tools like Databricks, SQL, Pandas, Snowflake and Collibra.

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.

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.

Develop, maintain, and publish Power BI reports and dashboards; extract, clean, and transform data; build tabular/multidimensional models and Azure data pipelines; perform ad-hoc analysis; manage SharePoint content; automate processes with Power Automate; create AI-driven analytics agents; ensure data quality, compliance, and support data-driven decision-making.

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.

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.

Hiring data professionals (Analyst, Engineer, Scientist) to extract, clean, model, and visualize data; build ETL/ELT pipelines, data lakes/warehouses; develop ML models; run A/B tests; provide insights via dashboards and cross-functional collaboration.

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.