Top Data Analyst Jobs in Chennai

Provides technical and business support for enterprise applications, resolving user issues, coordinating with technology teams and vendors, and supporting releases, testing, disaster recovery, and post-release validation. Analyzes application risks and vulnerabilities, maintains documentation, prioritizes remediation, and contributes to stability and efficiency initiatives. Requires advanced SQL, data warehousing, mixed operating system, incident management, and financial markets knowledge, along with strong communication and stakeholder management skills.

Leads enterprise data analytics programs and PMO governance, overseeing project tracking, milestones, budgets, resources, RAID management, executive reporting, reconciliation frameworks, and exception workflows. Acts as a senior bridge between business, finance, technology, risk, audit, and operations stakeholders. Manages and develops a team of 6–10 business analysts, project managers, and data analysts while ensuring compliance, delivery quality, and accountability.

Leads a data analytics and PMO function overseeing a multimillion-dollar program portfolio. Governs project tracking, milestones, budgets, resources, risks, and executive reporting. Directs reconciliation and exception-engineering frameworks, data lineage mapping, requirements authoring, and analytics delivery. Partners with senior stakeholders across Markets, Finance, Technology, Risk, Audit, Operations, and Quantitative teams. Manages and develops a team of 6–10 business analysts, project managers, and data analysts while ensuring regulatory compliance and delivery accountability.

Leads daily data platform operations, including support queue management, ticket prioritization, incident escalation, SLA monitoring, root-cause analysis, stakeholder communication, runbook development, governance support, and operational reporting. The role requires production support experience and familiarity with JIRA, Snowflake, Power BI, Qlik, data warehousing, incident management, and service delivery.

Analyze, cleanse, validate, and migrate Engineering and CPQ data for a global Microsoft Dynamics 365 rollout. Develop Power BI and Excel dashboards, manage ETL workflows, identify trends and anomalies, and maintain data quality through migration cycles. Present findings to senior stakeholders, support production cutover and early-life operations, and identify process improvements. The role requires strong database, programming, data profiling, master data management, migration, visualization, problem-solving, and communication skills.

Manages and governs data assets by supporting data strategy, quality, standards, access, validation, standardization, protection, and publication. Works on limited-scope data management projects, follows established procedures, resolves routine technical issues, and supports trusted data use for regulatory and business goals. The role may require multiple shifts, including late-night shifts.

Build, maintain, and optimize enterprise data platforms using Databricks, Apache Spark, cloud technologies, and DevOps tools. Develop CI/CD pipelines, support cloud migration and automation, monitor platform health, resolve incidents, and ensure security, governance, reliability, and operational standards. Collaborate with engineers, architects, product teams, stakeholders, and global support teams while participating in rotational production support shifts.

Supports enterprise data platform engineering through Databricks and Spark workload management, cloud platform operations, CI/CD automation, monitoring, incident resolution, production support, and platform modernization. The role collaborates with engineering, architecture, product, and business teams while maintaining security, governance, reliability, documentation, and operational standards. Rotational shifts provide global support coverage.

Build and maintain OneStream forecasting, planning, reporting, and dashboard capabilities for Commercial FP&A. Develop demand planning models, reports, reconciliations, and performance monitoring processes while supporting month-end reporting and data improvement initiatives. Partner with finance, data, administration, and implementation teams to optimize the platform, automate processes, troubleshoot performance, deliver actionable insights, and develop internal OneStream expertise. The role also evaluates AI and automation opportunities across FP&A workflows.

Supports platform operations by troubleshooting customer and partner issues, managing cases in Salesforce, improving workflows, maintaining documentation, and coordinating project updates. The role collaborates with internal teams and external partners on HL7 integrations, APIs, Data View, and other platform services. It also identifies recurring issues, supports process improvement, and helps develop operational solutions using SQL, AI tools, and automation opportunities.

Analyze courier supply and dispatch operations across onboarding, retention, availability, earnings, assignment, acceptance, and delivery success. Build reliable metrics, dashboards, and self-service reporting; identify root causes of performance changes; evaluate experiments, incentives, and product initiatives; validate data quality; and translate findings into recommendations for product, operations, and leadership.

Provide ad-hoc data and reporting support to Sales and internal teams using SQL, Databricks/Count, and spreadsheets. Clarify requirements, investigate discrepancies, resolve basic data issues, support routine data operations, and create reusable queries/reports and documentation to enable self-service.

Analyze structured and unstructured data to identify trends, build dashboards and reports, define KPIs, validate data quality, and provide actionable recommendations. The role requires SQL, BI visualization, Excel, relational database experience, and collaboration with business and technical stakeholders. Preferred skills include Python, ETL and data warehousing, cloud platforms, statistical analysis, forecasting, machine learning concepts, and Agile experience.

Own analytics and measurement for HighLevel’s AI product portfolio. Define instrumentation standards, feature-level KPIs, adoption and quality metrics, and rigorous approaches for evaluating nondeterministic AI features. Partner with experimentation leaders, PMs, engineering, and Analytics Engineering to close data gaps, influence product roadmaps, establish canonical metrics, and mentor future analysts.

Audits and validates labeling annotation data for accuracy, consistency, guideline adherence, and regulatory compliance. Identifies errors and root causes, monitors quality KPIs, leads governance cycles, translates data trends into process improvements, and leads the NC Program. Collaborates with agencies and internal stakeholders across labeling operations.

Maintains and governs account and customer master data across Salesforce and enterprise systems. Responsibilities include creating and enriching records, managing hierarchies, resolving duplicates, investigating data-quality issues, conducting audits, maintaining governance controls and documentation, and partnering with cross-functional teams. The role also supports process improvements, system changes, business transformation initiatives, and provides master data expertise.

Investigate and resolve complex data discrepancies across multiple systems, validate and reconcile information, perform corrections, document findings, and meet quality and turnaround targets. The role identifies trends and process gaps, supports escalations, conducts peer reviews, guides junior analysts, assists with training, and contributes to process improvements and automation initiatives.

Evaluates, acquires, and manages external healthcare and pharmaceutical data sources for Roche’s U.S. commercial and strategic teams. Translates stakeholder needs into data requirements, assesses vendors and datasets for quality and business fit, investigates data issues, maintains documentation, and supports dataset onboarding and investment decisions. The role requires collaboration with U.S. stakeholders, external vendors, and cross-functional teams, along with structured analysis, stakeholder management, and knowledge of pharma and healthcare data ecosystems.

Lead data science projects: analyze banking data, build ML solutions, design data displays, consult with stakeholders, manage requirements-to-implementation, coach junior staff, and ensure compliance and data quality.

Own marketing database operations for EMEA, including audience segmentation, data quality, privacy compliance, and performance analysis. Translate complex marketing data into actionable insights, recurring reports, and segmentation models. Partner with Marketing Operations, IT, and Data teams to improve database accuracy and processes, while enabling stakeholders through clear communication and training. The role requires strong SQL, marketing technology, project ownership, and business-facing analytical skills.

Conduct a three-hour interactive online training session on qualitative data analysis, including thematic and content analysis, coding, codebook development, identifying themes, transcript and focus group analysis, practical case studies, and qualitative analysis tools.

Supports data pipeline development and maintenance, performs foundational data engineering and processing tasks, follows data quality and security standards, and collaborates on data initiatives while developing expertise under senior guidance.

Manage, clean, validate, and maintain programme datasets; analyze trends and performance gaps; develop dashboards, trackers, and reports; support monitoring and evaluation; and improve data collection workflows. The role provides actionable insights to state leadership and supports reviews with government and internal stakeholders. It requires strong Google Sheets and Excel skills, with visualization and SQL knowledge preferred, plus Tamil reading and writing proficiency.

Transforms complex datasets into strategic insights supporting revenue growth and executive decision-making. Responsibilities include defining KPIs, building analytical models, creating dashboards and reports, analyzing customer behavior and revenue, identifying risks and opportunities, conducting cohort, retention, funnel, and forecasting analyses, and partnering with business stakeholders and data engineering teams.

Lead the design, modernization, operation, and support of enterprise platforms across UNIX, Linux, Windows, cloud, and hybrid environments. Manage servers, virtual machines, storage, databases, workload scheduling, middleware, managed file transfers, reporting platforms, automation, security, monitoring, disaster recovery, and major incidents. Drive platform stability, resiliency, observability, compliance, performance, and operational excellence while partnering with infrastructure, application, data, cybersecurity, and business teams.