Top Data Analyst Jobs in Hyderabad

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).

Own and enhance a sales crediting platform supporting incentive compensation. Build scalable SQL and Python data pipelines using Databricks, implement validation and reconciliation frameworks, troubleshoot production issues, and deliver analytics and dashboards. Partner with business, IT, and data stakeholders to translate requirements into technical solutions, automate processes, manage platform enhancements, and explore AI applications for anomaly detection, data quality, and operational efficiency.

Leverage data to support strategic decisions: gather, clean, and integrate data from multiple sources; perform statistical analyses and A/B tests; build automated dashboards and reports; develop predictive models and forecasts; measure marketing funnel and campaign effectiveness; collaborate with data engineers and stakeholders; mentor junior analysts.

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.

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.

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.

Leads reinsurance settlement operations, client reporting, reconciliations, data integrity, controls, and risk resolution. Uses Power BI, Alteryx, Power Query, AI tools, and automation to analyze large datasets, improve reporting, modernize workflows, reduce cycle times, and strengthen operational controls. Partners with Finance, Technology, Operations, and vendors on system integrations, UAT, process redesign, documentation, and continuous improvement.

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.

Prepares, validates, reconciles, and quality-checks operational reports, dashboards, and management information outputs. Verifies source data, calculations, formatting, and consistency across systems; identifies anomalies and coordinates corrections. Maintains reporting documentation and quality-control procedures while improving validation processes, automating repetitive checks, and ensuring accurate, timely report delivery.

Manage and improve master data management platforms, ensuring data accuracy, consistency, quality, governance, and compliance. Design data integration capabilities, support AWS and related platforms, perform profiling and cleansing, oversee migrations, maintain documentation, and develop Python-based data transformations. Collaborate with IT, MDM, DevOps, and business stakeholders to implement data pipelines, automate workflows, monitor performance, and resolve data quality issues.

Analyzes and develops software applications across the full development lifecycle, including requirements gathering, technical design, coding, testing, troubleshooting, documentation, deployment, and support. Collaborates with solution architects, project leads, and client teams while following Agile/Scrum practices. Requires strong debugging, problem-solving, communication, consulting, and independent-working skills, plus a bachelor's degree in a relevant technical field.

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.

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.

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.

Build production-grade Silver and Gold data products from architectural designs and business requirements. Develop reusable data solutions using Databricks, Spark, SQL, and Python; implement dimensional and medallion modeling, automated data-quality testing, observability, CI/CD promotion, and performance optimization. Partner with architecture, analytics, governance, platform engineering, and business teams while applying software-engineering discipline, AI, and automation in regulated enterprise environments.

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.

Develop and optimize SQL queries, data pipelines, and integrations using Azure Data Factory, Databricks, ADLS, Python, and PySpark. Implement data quality checks, troubleshoot production issues, monitor SLAs, support CI/CD and release management, and maintain documentation. Collaborate with technical and business stakeholders, lead client engagements, present findings, and support data delivery across systems including Veeva CRM and Salesforce.

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.

Maintains R&D portfolio reporting infrastructure, data quality, KPI definitions, semantic standards, and management information systems. Collaborates with data governance and digital portfolio teams on MDM, migrations, data transitions, and AI-ready data practices. Analyzes complex datasets, identifies trends and anomalies, creates leadership reports and visualizations, documents workflows, and recommends data management improvements.

Design and implement enterprise-scale data pipelines and governed Power BI datasets; build analytics dashboards using Microsoft Fabric and Snowflake; integrate AI tools (Copilot Agents/Claude) for insights; enforce data governance, quality, and performance optimization; collaborate with finance and cross-functional stakeholders.

The role involves managing a team of data analysts to ensure the quality and usability of healthcare market datasets, implementing analytical standards, and translating complex data outputs into actionable business insights.

Leads the design, development, optimization, and support of enterprise data engineering solutions across Oracle, PostgreSQL, and cloud platforms. Responsibilities include building data models and ETL pipelines, optimizing database performance, ensuring data quality and governance, troubleshooting production issues, supporting modernization initiatives, and providing technical leadership, mentoring, and architectural guidance to distributed engineering teams.