Top Data Analyst Jobs in Bangalore

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 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.

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.

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.

Evaluates AI outputs and workflows through human labeling, failure-mode analysis, benchmark maintenance, and quality reporting. The role maintains labeled datasets, golden sets, and regression slices; calibrates human graders; validates LLM-as-a-judge results; and identifies hallucinations, retrieval, tool-use, grounding, and workflow issues. It partners with Engineering, Product, QA, and evaluation teams to improve rubrics, assess product changes, monitor quality, and support release-readiness decisions.

Analyze, profile, map, and visualize business risk data across its lifecycle. Translate risk requirements into data solutions, automated processes, data models, and self-service analytics. Document risk processes and data flows, ensure data quality and consistency, and deliver qualitative and quantitative insights. Collaborate with Business Risk and other stakeholders to support data-, automation-, and AI-driven initiatives.

Develop and maintain enterprise-scale Power BI dashboards, reports, semantic models, and workspaces. Use Microsoft Fabric, Python, Dataiku, GCP, BigQuery, and SQL to ingest, transform, automate, and analyze data. Partner with stakeholders to define KPIs and deliver actionable insights while ensuring data governance, quality, security, documentation, refresh management, and performance optimization. Support scalable self-service analytics and leverage modern AI-enabled capabilities.

Develops dynamic client-facing reports and visualizations for investment services clients using IRD, MCD, and BI platforms. Analyzes large datasets, supports data modeling and data mart design, applies SQL for validation and optimization, and collaborates with data and product teams to ensure data integrity. Serves as an IRD subject matter expert while supporting reporting issues and complex global requirements across custody, digital, and fund services.

Provides lead-level data analysis and cloud platform consulting support using Oracle Cloud, integration platforms, ETL tools, DevOps technologies, and related cloud infrastructure tools.

The posting provides extensive information about Quess Corp and its business divisions but does not specify the Assistant Manager Data Analyst role’s responsibilities, required qualifications, tools, compensation, or work arrangements.

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.

Own the company’s data foundations and self-serve analytics strategy. Design and maintain event-based data models, scalable datasets, dashboards, monitoring, and alerting systems. Build and validate analytics solutions from raw pipelines through executive dashboards, improve data reliability, and drive adoption across teams. Identify opportunities to integrate AI into analytics workflows and translate technical concepts into actionable insights for non-technical stakeholders.

Analyze marketing data to measure campaign performance, ROI, attribution, customer behavior, and funnel conversion. Build Tableau dashboards and reports, define KPIs and measurement frameworks, evaluate experiments, and provide recommendations for campaign strategy and budget allocation. Translate complex findings into executive-ready presentations and collaborate with global marketing stakeholders across digital, social, email, and event channels.

Leads a team of analysts and oversees advanced data analysis to support sales performance and strategic decision-making. Analyzes large datasets, develops data models and predictive algorithms, creates visualizations and reports, collaborates with cross-functional stakeholders, and ensures data integrity through cleansing and validation.

Leads a team of analysts and performs advanced analysis on large datasets to identify trends, develop predictive models, and generate strategic recommendations. Collaborates with sales, marketing, and executive stakeholders to support data-driven decisions, optimize sales performance, improve efficiency, and drive business growth. Ensures data integrity through cleansing and validation while communicating insights through reports and visualizations.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.

Analyze usage, quality, and performance data across language services. Build dashboards and reports, conduct exploratory analysis on large multilingual datasets, identify data gaps and quality issues with ML and engineering teams, communicate insights to stakeholders, and maintain data documentation, validation checks, and quality standards. The role requires SQL, Python, and visualization tools such as Power BI or Tableau, with NLP, experimentation, and big data experience preferred.

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.

Manage and analyze product master data across enterprise systems and MDM platforms. Define data quality rules, perform profiling and source-to-target reconciliation, resolve data issues, support onboarding and migration, and maintain governance documentation. Partner with Product, Engineering, Business, and Data Governance teams on UAT, remediation, automation, and continuous improvement. Use SQL, Reltio, Snowflake, Ataccama, and AI/GenAI tools to improve data analysis and quality management.

Leads enterprise data governance, quality, warehousing, reporting, and analytics initiatives for US banking regulatory data. Responsibilities include data profiling, trend analysis, exception reporting, issue resolution, dashboard development, month-end validation, policy enforcement, source-to-target mapping, data dictionaries, and stakeholder consultation. The role requires advanced SQL, banking data knowledge, ETL and data warehousing expertise, and collaboration across business and technology teams.

Analyze product performance, user behavior, funnels, cohorts, retention, LTV, and experimentation results to guide product decisions. Build dashboards, reports, data marts, dimensional models, and semantic layers for self-service analytics. Partner with Product, Engineering, Marketing, and Business teams to define KPIs, identify growth opportunities, improve engagement, and support monetization. Use SQL, Python, BI tools, and AI-assisted workflows to deliver accurate, actionable insights.

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.

Designs and delivers data requirements supporting accurate clinical trial site payments across IQVIA systems. Interprets EDC visit data, site contracts, payment triggers, and system designs; analyzes data fallout, performs corrections, resolves data flows, and escalates system issues. Collaborates with IT, Payments, Data Alignment, and other internal stakeholders to troubleshoot CTMS, DrugDev, data warehouse, and EDC issues affecting payments.

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.

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.