Top Analyst Jobs in Bangalore

Own analytical workstreams for marketing solutions consulting engagements. Prepare, validate, and analyze complex datasets; support Marketing Mix Modelling; develop forecasts and recommendations; create Excel and PowerPoint deliverables; communicate findings to stakeholders; manage timelines and quality assurance; and coach junior analysts. Collaborate with consulting, data, modelling, engineering, and global delivery teams.

Bridges product, business, operations, and technology teams by eliciting requirements, translating them into technical solutions, analyzing large datasets, and developing BI dashboards. Maintains architecture documentation, governance processes, and data-driven insights while managing dependencies and change control. Uses and validates enterprise-approved AI capabilities, documents user stories and acceptance criteria in JIRA, supports continuous improvement, and guides others in delivering timely, scalable solutions.

The Staff Data Analyst will transform data into insights for GitLab’s Support Engineering organization. Responsibilities include capacity and budget modeling, utilization scorecards, support economics analysis, performance indexes, AI adoption ROI dashboards, predictive churn analytics, customer-schema standardization, executive reporting, and ad hoc analysis. The role partners with Support Engineering, go-to-market, Business Operations, and Product Engineering in a remote environment.

Analyze renewal pipelines, customer health, churn, upsell, retention, and business performance using data. Develop forecasts, identify trends and gaps, validate data, recommend action plans, and provide insights to Sales and Customer Success leadership. Drive process improvements, automation, KPI reporting, and cross-functional operational programs while presenting business outcomes to stakeholders.

Own analytics for Words With Friends Instant, providing insights on player behavior, product performance, experimentation, revenue, retention, and engagement. Design statistical experiments, dashboards, telemetry, and ETL pipelines; analyze trends and feature health; maintain data tools and models; and communicate recommendations to product, engineering, design, and executive stakeholders. The role also supports AI initiatives and optimization of the player experience.

Monitor and validate Power BI and Qlik dashboard refreshes, investigate data discrepancies, troubleshoot failures, and coordinate resolutions with data teams. Maintain logs, SOPs, issue trackers, and knowledge documentation while supporting dashboard enhancements, performance improvements, governance, audits, and stakeholder queries. The role requires SQL-based data validation, root-cause analysis, dashboard monitoring, and communication with business and technical stakeholders.

Develops and optimizes risk reports for Commercial Real Estate business teams. Translates stakeholder requirements into meaningful reporting outputs, performs advanced data analysis and complex querying, identifies reporting capabilities, and supports process improvement. Collaborates across business lines, manages multiple initiatives, influences stakeholders, and applies business analysis, design thinking, statistical methods, and project management skills.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Build and maintain Power BI finance and FP&A dashboards, semantic models, datasets, and DAX calculations. Transform and validate data from Dataverse, Cosmos DB, and enterprise systems using Power Query and SQL. Develop reporting on revenue, profitability, budgets, forecasts, costs, and variance analysis. Ensure data quality, governance, security, and performance while documenting reporting logic. Partner with Finance, FP&A, Data, Technology, and commercial teams to translate business requirements into scalable insights and automate reporting processes.

Analyze and model data from multiple sources, build reliable datasets and dashboards, validate data quality, conduct statistical analyses and experiments, and translate findings into business recommendations. Partner with cross-functional stakeholders across marketing, operations, finance, and product. Promote data literacy, document best practices, and support data-driven decision-making. The role requires strong SQL, Python or PySpark, BI tools, Databricks, financial services knowledge, and experience with credit risk, lending, governance, and customer lifecycle analytics.

Supports AI initiative delivery by identifying business opportunities, gathering requirements, translating needs into technical specifications, assessing business value, and coordinating stakeholders, data scientists, engineers, IT teams, and vendors. The role also supports Azure AI solution delivery, data governance, ethical AI compliance, change management, user adoption, documentation, and process optimization.

Senior Data Analyst responsible for defining product metrics, analyzing complex datasets, conducting A/B tests, identifying business opportunities, and translating insights into recommendations. The role partners with product, design, engineering, and business leaders to support data-driven decisions, develop analytical methodologies, communicate findings to executives, and use AI tools to improve analytical productivity.

Leads complex analytics initiatives, translates business needs into scalable data solutions, develops KPI frameworks, and delivers actionable insights through SQL, Python or R, dashboards, and reporting. Partners with senior stakeholders on analytical roadmaps, mentors junior team members, and supports data-driven decision-making. Responsibilities include data extraction, manipulation, automation, statistical modeling, data visualization, performance tracking, and business reporting.

Develop and execute marketing analytics strategies, oversee Python-based ETL pipelines and SQL data environments, maintain Tableau and Adobe Analytics reporting solutions, establish data governance and quality standards, define KPIs, deliver actionable insights, manage analytics projects, and present findings to leadership.

Defines product metrics and measurement frameworks, analyzes complex datasets, conducts statistical analyses and A/B tests, and translates findings into actionable recommendations. Partners with product, design, engineering, and business leaders to prioritize initiatives and improve outcomes. Develops scalable analytical methods, communicates insights to executive stakeholders, and uses AI tools to automate workflows and increase analytical productivity.

Analyzes, cleans, validates, and interprets data from multiple sources to generate actionable business insights. Uses Python or R, SQL, Excel, and Power BI or Tableau to perform analysis, automate processes, and create dashboards and reports. Identifies trends and KPIs, ensures data accuracy, communicates findings, and collaborates with cross-functional stakeholders to support data-driven decisions.

Supports the enterprise data systems ecosystem by monitoring performance, managing system changes, troubleshooting incidents, resolving data and integration issues, administering access controls, maintaining documentation, and optimizing workflows through SQL, reporting, ETL, and automation tools. The role partners with business and technical teams on implementations, testing, upgrades, training, and continuous improvement.

Analyze business data, build and maintain business models, provide actionable insights and reports, collaborate with cross-functional teams, monitor performance, and recommend efficiency improvements to support strategic decision-making.

Leads enterprise MDM and PIM implementations by facilitating discovery, documenting requirements, designing data governance and migration strategies, configuring platforms, and managing UAT. Profiles legacy data, defines mappings and quality rules, maintains traceability, coordinates stakeholders and vendors, manages scope and defects, creates documentation, and trains client teams. The role requires rapidly mastering MDM platforms, using AI tools responsibly, and translating technical constraints into business terms.

Leads end-to-end analytics for content-flow operations, translating business questions into hypotheses, KPI frameworks, dashboards, and actionable insights. Analyzes throughput, cycle times, quality, rework, supplier performance, workload, and operational bottlenecks using SQL and advanced analytical methods. Partners with senior stakeholders, data management, IT, and operations teams to improve data quality, governance, reporting adoption, and decision-making. Coaches junior analysts and leads tactical analytics projects.

Analyze existing data structures, map to new designs, write user stories and acceptance criteria, document data mappings/transformations, gather requirements, support developers and testers, and run workshops to ensure business-focused delivery.

Leads AI-driven reporting, analytics, and digital initiatives across the full project lifecycle. Designs dashboards, data architectures, ETL/ELT pipelines, APIs, and business intelligence solutions using cloud and AI technologies. Partners with stakeholders to define requirements, prioritize projects, manage delivery, and communicate technical concepts. Evaluates emerging tools, integrates agentic AI into reporting workflows, and drives continuous improvement, adoption, and measurable business value.

Lead MDM data consumption and validation across Customer 360/Reference 360. Validate data flows from MDM to APIs, batch pipelines and Snowflake; run SQL-based reconciliations; support UAT, defect tracking, requirements documentation, and downstream financial reporting validation. Act as analyst bridging producers and consumers, focusing on data quality and documentation rather than development.

Design, develop, and support BI solutions, dashboards, and reports. Build SQL queries, data models, and ETL/ELT pipelines (Databricks). Analyze data for trends and KPIs, ensure data quality, automate reporting, support UAT and deployments, and train business users.

Lead data analytics initiatives by developing Tableau and Looker dashboards, integrating and validating data from multiple sources, optimizing reporting performance, writing SQL and Python-based analyses, translating stakeholder needs into scalable solutions, and mentoring analysts. The role also supports data engineering, ETL/ELT processes, GCP applications, and continuous improvement of analytics practices.