Top Data Analyst Jobs in Hyderabad

Lead data quality, governance, metadata, controls, and remediation initiatives for Commercial Banking and Lending data. Analyze datasets with SQL, identify defects, perform root-cause analysis, assess data controls, define quality metrics, and support regulatory reporting. Partner with business and technology stakeholders on solution design and remediation, develop metadata and data dictionaries, coordinate cross-functional efforts, report to management, and mentor less experienced team members.

Designs, builds, and operates scalable ETL/ELT pipelines and cloud-native data platforms using Databricks and Snowflake. Develops trusted analytical datasets, ensures data quality and governance, optimizes performance and cost, modernizes legacy pipelines, and supports analytics and AI/ML workloads. Partners with stakeholders, applies software engineering practices, troubleshoots production issues, contributes to architecture, mentors junior engineers, and develops responsible AI-powered solutions.

Supports CIB data services, governance, dataset registration, metadata and lineage management, data quality improvement, and control activities. Partners with business and technology stakeholders to define requirements, manage roadmaps and deliverables, coordinate testing and adoption, and prepare executive-ready reporting. Applies project management, risk management, process analysis, and responsible AI or automation techniques to improve data transparency, traceability, quality, and reuse.

Provides entry-level data analysis, reporting, data maintenance, validation, reconciliation, and quality assurance support. Reviews information for discrepancies, prepares reports and dashboards, summarizes trends and variances, monitors reporting requests and data issues, maintains documentation, and escalates exceptions. The role follows established procedures while developing knowledge of company systems, data standards, and business processes.

Analyze and reconcile customer accounts, contracts, opportunities, and renewal data across disconnected systems. Build defensible performance baselines, renewal and erosion analyses, parity rates, forecasting rollups, capacity inputs, and QBR analytics. Document data lineage and methodology, support implementation teams, and defend analytical conclusions with customer finance stakeholders. The role also translates reporting requirements into customer consoles and maintains consistent reporting across delivery teams.

Designs and implements cloud data pipelines, databases, CI/CD frameworks, and process automation across analytics platforms. Uses Databricks, Azure, PySpark, GenAI tools, and DevOps practices to improve scalability, automation, delivery speed, and client self-service. The role integrates enterprise data and analytics applications, supports technical delivery as an individual contributor, and requires effective team communication and policy compliance.

Analyze homeowners insurance data to support pricing, underwriting, retention, product, and competitive intelligence decisions. Write SQL across BigQuery and SQL Server, build Power BI dashboards, identify portfolio trends and anomalies, measure product changes, and ensure data quality through QA and reconciliations. The role partners with product managers and business leaders, requiring autonomous problem-solving, clear communication, and cross-functional analysis.

Provides entry-level data analysis, reporting, data validation, reconciliation, quality assurance, and operational support. Maintains accurate records and reports, identifies discrepancies, summarizes trends, monitors reporting requests, and coordinates issue resolution with stakeholders. Follows established procedures, data governance standards, and business processes while developing knowledge of company systems and reporting practices.

Design, build, and maintain scalable Azure data engineering solutions (Databricks, ADF, Snowflake). Develop ETL/ELT pipelines, batch and near real-time workflows, CI/CD and orchestration, and ensure data quality, security, and compliance in healthcare. Support production/on-call, collaborate with stakeholders on architecture, and apply GenAI techniques (RAG, LangChain). Contribute to API development (FastAPI) and containerized delivery (Docker).

Conduct media, social listening, influencer, and predictive analytics research for agency clients. Manage recurring and ad hoc KPI reporting, trend analysis, crisis monitoring, and optimization recommendations. Translate data into strategic insights and compelling stories, advise account teams and leadership, and mentor junior analysts. Support consumer, technology, healthcare, and food and beverage accounts while using specialized social listening tools and Boolean queries.

Generates and manages accurate data for reports, oversees coding quality and junior coding teams, manages content searches and uploads, performs data checks, troubleshoots data-flow issues, creates charts and qualitative insights, and supports data exports, contact management, verification, and report production.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Designs, implements, executes, and monitors data quality processes and issue-management capabilities. Responsibilities include maintaining data quality agreements, developing and refining data quality specifications, collaborating with stakeholders, designing technical and procedural solutions, reporting metrics, supporting DevOps activities, and participating in agile release cycles. The role requires experience with data management, ETL, data quality tools, SQL, Tableau dashboards, and financial-services data.

Analyze large-scale audience, client, and vendor datasets to assess data quality, matching accuracy, connectivity, and coverage. Use SQL, Python or R, statistical analysis, exploratory analysis, and validation methods to identify anomalies, false positives, false negatives, and improvement opportunities. Create benchmarking and regression datasets, validate audience outputs, report insights, and collaborate with Product, Engineering, and Data Science teams to improve audience data products.

Analyze identity graph outputs and large datasets to detect duplicates, anomalies, fragmented identities, and incorrect household groupings. Use SQL, Python, or R to validate matching behavior, assess precision and recall, create benchmark and regression datasets, and investigate false positives and negatives. Develop reports and dashboards, perform statistical analysis, and collaborate with Product, Engineering, and Data Science teams to improve identity resolution quality and graph performance.

Designs, implements, optimizes, and maintains ETL and data integration workflows. Collaborates with business and technical teams to deliver data transformation solutions, conducts proof-of-concept research, validates data quality, tunes database performance, monitors Databricks and Airflow processes, and documents technical solutions. The role requires Python, cloud, DevOps, relational and NoSQL database, data warehouse, data lake, and lakehouse experience.

Design, build, and maintain data extraction and transformation pipelines using Azure Data Factory and Databricks. Develop complex SQL (joins, CTEs) and PySpark code, ensure data quality and business logic, troubleshoot production issues, manage CI/CD via Azure DevOps, integrate with Veeva/Salesforce, meet SLAs, document processes, and support EST shift hours while collaborating with technical and business stakeholders.

Analyze complex datasets, develop and optimize SQL queries and Databricks workflows, perform data validation and quality checks, monitor production workflows, and build data quality dashboards. The role delivers actionable insights, supports automation and data governance, resolves data issues, and collaborates with engineering, business, vendor, and cross-functional stakeholders to improve data products and reporting.

Develop automated data solutions using VBA, SAP Scripting, Power Query, Power BI, and Python. Build dashboards, reports, data models, ETL processes, and Excel automation tools. Translate stakeholder requirements into actionable insights, optimize reporting performance, troubleshoot code, and collaborate with data teams. The role also involves data manipulation, API integration, and potentially SQL, relational databases, Tableau, QlikView, and Alteryx.

Bridges business strategy, data analytics, and AI by gathering requirements, building executive dashboards, identifying AI opportunities, optimizing processes, supporting UAT and implementations, and translating complex data into actionable recommendations. The role also facilitates stakeholder workshops, creates training materials, evaluates AI solution performance, and helps operationalize data and AI initiatives that reduce costs and generate measurable business value.

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.

Lead workforce analytics to inform talent strategy and workforce planning. Build dashboards, forecasts, and predictive models; integrate Workday, Databricks, SQL, and Excel data; present insights to HR and business leaders; improve data governance, automate reporting, and mentor junior analysts.

Lead enterprise data analytics initiatives by designing scalable data pipelines, cloud-based data platforms, governed Power BI datasets, and advanced analytics dashboards. Translate finance and business requirements into data strategies, optimize modeling and orchestration, and uphold data quality and governance standards. Collaborate with global stakeholders while promoting technical excellence and developing data engineering capabilities. The role uses Snowflake, Microsoft Fabric, Power BI, Python, and AI-powered tools such as Copilot Agents and Claude.