Top Analyst Jobs in Bangalore

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.

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.

Manage material master data and governance processes in SAP S/4HANA MDG. Validate material attributes, plant extensions, BOM updates, integrations, and data migrations across Agile, SAP, and planning systems. Support ECO/ECN implementation, migration testing, data reconciliation, defect resolution, workflow tracking, and status reporting while coordinating with product configuration, planning, manufacturing, and plant extension teams.

Leads customer analytics projects using dbt and Snowflake, establishes data and code quality standards, mentors junior analysts, conducts code reviews, documents reusable solutions, manages customer requirements, delivers analytical findings, and identifies expansion opportunities.

Manage Supplier, Customer, and Material Master Data in SAP, including onboarding, maintenance, deactivation, data cleansing, quality remediation, governance, incident resolution, and SLA compliance. Partner with Data Stewards and global business teams across regions to resolve data issues, monitor quality KPIs, support SAP operations, document processes, and improve master data governance and operational efficiency.

Develop and maintain Power BI dashboards, reports, data models, and refresh processes. Write and optimize SQL queries, perform data validation and quality checks, and collaborate with IT and data engineering teams. Translate stakeholder requirements into reporting solutions, maintain documentation, follow reporting standards, and deliver work within project timelines.

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.

Analyze and interpret large healthcare datasets, build and maintain data pipelines and structures, collaborate with cross-functional teams to deliver actionable insights, and document data processes and analyses.

Build, maintain, and optimize enterprise data platforms using Databricks, Spark, cloud technologies, and DevOps practices. Develop CI/CD pipelines, support cloud migration and automation, monitor platform health, troubleshoot incidents, and ensure reliable, secure, and scalable data services. Participate in rotational production support across global time zones, perform root cause analysis, maintain operational documentation, and collaborate with engineering, architecture, product, and business teams.

Own quantitative market intelligence for global DRAM, HBM, and NAND Flash markets. Develop supply-demand, capacity, pricing, revenue, market-share, technology-transition, and application-demand forecasts. Maintain market models, trackers, databases, and scenario analyses; identify forecast risks and revise outlooks. Translate complex semiconductor data into written research, actionable recommendations, executive presentations, and client guidance while collaborating with adjacent technology analysts.

Own payer, plan, and provider data mapping into governed hierarchies. Research and enrich records, validate data quality, document lineage and metadata, develop governance standards and dashboards, resolve exceptions, and quality-check junior analysts’ work. Collaborate with product, engineering, content, delivery, operations, and client success teams to improve data products and operationalize AI/ML-assisted mapping while maintaining human oversight.

Owns sales reporting, dashboards, forecasting, pipeline analytics, and advanced data science solutions. Develops predictive models, AI-enabled insights, scoring, segmentation, and automation to improve revenue performance. Partners with Sales, Finance, and Operations, enhances data quality and governance, translates complex analyses into business recommendations, and mentors analysts.

Design and deliver analytics solutions by writing and optimizing complex SQL, building interactive Power BI/Tableau dashboards, performing data analysis to surface insights, working with stakeholders to translate requirements, ensuring data accuracy and performance, and automating reporting and data workflows.

Build and maintain scalable ELT/ETL pipelines for high-frequency power system data, automate simulation-result extraction with Python, manage PostgreSQL schemas and data-lake architectures, and create Power BI or Tableau dashboards. The role partners with power systems engineers to connect simulation outputs with grid reliability and economic insights. It also supports data modeling, warehousing, business intelligence, cloud data environments, and internal data-driven initiatives.

Maintain and govern enterprise master data by defining standards, integrating systems, supporting SAP MDM/MDG users, translating business needs into technical designs, training users, coordinating consultants, and improving data lifecycle and system configurations.

Maintain accuracy, governance, and lifecycle of enterprise master data in SAP environments. Define data standards, map system integrations, support global SAP MDG/MDM users, collaborate with Supply Chain/Finance/IT, design technical solutions, provide training, coordinate consultants, and ensure data quality and system controls.

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.