Top Analyst Jobs in Bangalore

Designs, develops, tests, deploys, and supports enterprise analytics solutions, including data pipelines, curated datasets, semantic models, Power BI dashboards, reports, and DAX calculations. Partners with engineers, QA teams, product owners, and business stakeholders to define requirements, validate data, troubleshoot issues, optimize performance, document solutions, and support UAT and releases. Provides technical guidance to junior analysts and contributes to reusable standards, process improvements, and global team collaboration.

Analyze sales, customer, channel, shopper, and promotional performance to identify growth opportunities and improve trade investment productivity. Prepare reports, forecasts, business reviews, and actionable presentations for Sales and Trade Marketing teams. Support commercial planning, negotiations, process improvement, data governance, and IT integration while ensuring data accuracy and translating insights into market execution.

Own operational analytics and executive reporting by transforming delivery, quality, content-source, and API data into trusted dashboards and actionable insights. Responsibilities include delivery forecasting, defect and data-quality analysis, source and integration telemetry, ROI and capacity modeling, KPI definition, and reporting cadence management. The role partners with engineering, QA, DevOps, and operations teams to improve data reliability and inform planning, resourcing, and investment decisions.

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.

Lead sales analytics and revenue operations for B2C sales: build funnel reporting, dashboards (Mode, Hex, Amplitude), run A/B tests, ensure data quality, and partner on comp, headcount, and finance models. Integrate AI tools to accelerate analysis and automate workflows; work onsite in Bangalore night shift.

Analyzes business data, develops Power Apps, Power Automate workflows, Power BI dashboards, and data models, and translates stakeholder requirements into technical solutions. Performs data mining, root cause analysis, application support, documentation, and post-implementation improvement. Requires strong Power Platform, SQL, JavaScript, Power Fx, Dataverse, SAP, and analytics expertise, with cross-functional communication and business process knowledge. Manufacturing or inventory management experience is preferred.

Translate raw data into actionable product insights: build Tableau dashboards, maintain Snowflake/dbt data models and pipelines, write complex SQL and use Python for analysis, collaborate with stakeholders, and explore AI/ML to enhance analytics.

Own scalable data lifecycle and semantic models, enable data tooling, and deliver dashboards and product insights. Partner with Sales, TAM, Customer Success, Product, Engineering, and Program Management to define measurement strategies, document requirements, develop reusable business models, and promote privacy-conscious self-service analytics for Auth0 and enterprise stakeholders.

Develops and maintains business intelligence reports, dashboards, data models, and visualizations using Power BI, DAX, SQL, and enterprise data platforms. Extracts, transforms, validates, and analyzes data to generate actionable insights and support decision-making. Collaborates with stakeholders to gather reporting requirements, communicates findings, supports data governance, and improves reporting processes through automation and emerging technologies.

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.

Analyzes business and credit problems, coordinates data preparation, generates insights, recommends data-driven actions, influences stakeholders, and measures post-implementation impact. The role partners with leadership, credit, product, and data teams to support business growth and requires strong SQL, Python or R, and Excel skills.

Analyzes contact center, chatbot, and voicebot data to identify customer experience issues and optimization opportunities. Builds datasets, KPIs, dashboards, business reviews, and presentations using data from transcripts, bot logs, recordings, and operational metrics. Evaluates conversation flows, authentication, containment, sentiment, intent accuracy, and customer journeys. Measures pre- and post-optimization impact, estimates KPI improvements, supports hypercare, and collaborates with clients, leadership, delivery, and operations stakeholders.

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.

Maintains taxonomies, business glossaries, classification hierarchies, metadata, and testable data rules in a governance platform. Analyzes rule libraries for conflicts, duplicates, and coverage gaps; supports reference data certification, request triage, documentation, governance reporting, and stakeholder enablement. The role ensures governed artifacts are accurate, traceable, auditable, and operationalized across data quality and classification workflows.

Lead business intelligence and analytics for the Revenue Organization, partnering with Sales, Marketing, Customer Success, RevOps, and Finance. Own KPI frameworks, executive dashboards, self-service reporting, advanced SQL analysis, data quality, and analytical requirements. Enable stakeholders to use data and AI effectively, integrate LLM tools into BI workflows, support data modeling and governance, and manage analytics projects from requirements through adoption.

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.

Leads programmatic campaign analytics and strategy for clients and internal teams. Responsibilities include designing analytic and testing plans, monitoring campaign and model performance, developing reporting and measurement tools, recommending optimization opportunities, and presenting actionable insights. The role partners with Sales, Customer Success, Product, and Engineering teams while translating complex technical findings into clear client-facing marketing and campaign strategy recommendations.

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.

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.

Build and maintain large-scale geospatial datasets and ETL pipelines for production use. Perform GIS visualization, spatial analysis, data extraction, transformation, validation, and quality assurance using QGIS or ArcGIS, Python, PySpark, SQL, AWS, and geospatial databases. Automate workflows, document methodologies, support internal GIS requests, and collaborate with analytics and business teams to deliver accurate location-based data.

Manage and optimize data processes, ensuring quality and accuracy in financial data delivery, while enhancing operational efficiency. Act as a domain expert, build relationships with clients and teams, and support projects as assigned.

Implements and supports Adobe Customer Journey Analytics solutions, including data ingestion, schema alignment, identities, connections, data views, calculated metrics, and reporting readiness. Validates datasets and customer journey data, performs data quality audits, troubleshoots discrepancies, documents business rules, and partners with engineering, analytics, reporting, and business stakeholders. Requires strong SQL, AEP/CDP knowledge, analytics implementation experience, and stakeholder communication skills.

Develop and maintain Tableau dashboards, visualizations, reports, and BI solutions. Gather requirements from clients and stakeholders, define KPIs and metrics, analyze business processes, and translate business needs into analytical solutions. Perform SQL-based analysis, dashboard quality checks, validation, documentation, and data governance. Collaborate with Data Engineering teams, present insights through storyboarding, document meetings, and track work using JIRA.

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.