Top Data Analyst Jobs in Hyderabad

Analyzes OptumRx pharmacy operations, prescription fulfillment, order-cycle, financial, and performance data. Uses IRIS, SQL, Databricks, Snowflake, Tableau, Power BI, and MSBI to develop dashboards, reports, KPI scorecards, and actionable insights. Performs root-cause analysis, validates complex datasets, identifies process improvements, and supports operational efficiency and strategic decision-making across Home Delivery Pharmacy and Specialty Operations.

Leads data quality, governance, metadata, and control initiatives. Analyzes and remediates data integrity and process gaps, develops quality metrics and audits, assesses new data sources, coordinates remediation programs, consults stakeholders, supports governance strategy, and mentors junior staff.

Leads data quality, governance, metadata, and control initiatives; analyzes and remediates data integrity gaps; conducts audits and testing; develops quality metrics and remediation plans; assesses new data sources; coordinates cross-functional projects; consults stakeholders; and mentors junior staff.

Designs and maintains enterprise Power BI architectures, semantic models, security controls, advanced DAX and Power Query transformations, and SSRS reports. Integrates Python or R predictive models, Microsoft Fabric, Snowflake, Databricks, Copilot, AI visuals, and Azure Cognitive Services into dashboards and workflows. Develops advanced SQL transformations and communicates complex analytical outputs to business stakeholders.

Senior Data Analyst responsible for translating complex business questions into actionable insights. Duties include analyzing large datasets, building dashboards and reporting, defining metrics, supporting experimentation and forecasting, investigating data quality, and communicating recommendations to technical and non-technical stakeholders. The role partners cross-functionally with Product, Marketing, Finance, Content, and engineering teams, while mentoring analysts and improving analytical practices.

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.

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.

Analyze complex healthcare data to identify trends, drivers, and optimization opportunities. Translate business questions into analytical approaches, quantify impact, and deliver decision-ready recommendations. Partner with stakeholders, data engineering, and platform teams to ensure reliable data and scalable analytical assets. Communicate findings to technical, operational, and executive audiences while supporting data-driven decision-making and continuously improving analytical methods.

Leads economic modeling, traffic due diligence, toll revenue forecasting, econometric and time-series analysis, ETL architecture, data validation, and commercial recommendations. Presents findings to senior clients and investment committees, coordinates with traffic engineering teams, mentors analysts, manages project teams, and supports business development. Requires advanced Python, SQL, Excel, cloud analytics, reproducible data pipelines, and expertise in transport analytics and investment-grade modeling.

Build, deploy, monitor, and maintain scalable healthcare data solutions. Responsibilities include data acquisition, ETL/ELT development, transformation, warehousing, data quality management, pipeline monitoring, technical documentation, governance, security, and troubleshooting. The role partners with analytics and technology teams to support reporting, business intelligence, and advanced analytics while improving data architecture, reliability, and operational efficiency.

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.

Entry-level Product Data Analyst internship supporting product analytics: build and analyze conversion funnels, evaluate A/B tests, create dashboards, respond to ad-hoc data requests, write SQL pipelines, monitor data quality, and translate findings into visual summaries to inform product decisions.

Analyze business requirements, extract and investigate data, handle ad-hoc analytics requests, develop Qlik Sense dashboards, and present actionable insights. The role also improves platform stability, streamlines processes, supports business initiatives, and contributes to data science strategy. Candidates must collaborate effectively, solve problems proactively, and modify code as needed. Required technical skills include Python, PySpark, SQL, Qlik tools, and knowledge of S3 data architecture.

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.

Designs scalable, secure data architectures; performs proof-of-concepts; troubleshoots technology and environment issues; develops Azure cloud applications; and migrates BDPass data pipelines to Azure. The role requires cloud platform expertise, data security and privacy knowledge, programming in Python, Java, and SQL, big data technologies, data warehousing, modeling, data management, software development, and agile methodologies.

Designs scalable, secure cloud data architectures; performs technology proofs of concept, troubleshoots across environments, and migrates BDPass data pipelines to Azure. The role requires expertise in AWS and Azure, data engineering programming, big data technologies, data warehousing, modeling, security, software development, and agile practices, along with strong problem-solving, communication, and collaboration skills.

Manage and resolve client inquiries involving banking datasets, analyze data to produce actionable insights, and support quality assurance sampling workflows. Develop Power BI dashboards and use SQL and Python for advanced data manipulation, analysis, automation, and product enhancement. Collaborate with cross-functional global teams, maintain data integrity, and adapt to varying shifts across time zones.

Leads a small analytics team while analyzing complex business data, writing SQL queries, developing dashboards and MIS reports, tracking KPIs, validating data, and delivering actionable recommendations. Partners with business, product, operations, and technology teams to translate requirements into analytical solutions, automate recurring reports, and communicate insights to technical and non-technical stakeholders.

Participates in data processing, integration, pipeline development, data storage, and quality assurance. Collaborates with data engineers and analysts to support data-related projects, generate insights, maintain data standards, and improve engineering practices. Requires proven data engineering experience, continuous learning, and familiarity with relevant tools and technologies.

Internship data analyst supporting dashboard monitoring, user behavior and content ecosystem analysis, A/B test evaluation, campaign performance analysis, data quality assurance, and metric documentation. The role uses SQL and Excel to derive insights on engagement, retention, conversion, and creator ecosystems, while collaborating with product and operations teams in a blockchain and digital assets environment.

Build scalable data solutions, semantic models, data marts, transformation pipelines, dashboards, and analytics solutions using Microsoft Fabric, Snowflake, dbt, Power BI, and Tableau. Analyze business, financial, and operational requirements, deliver data-driven insights, support AI-enabled analytics, and collaborate with stakeholders across the organization.

Support Finance and Central Operations by building dashboards, reporting, automated data pipelines, reconciliations, controls, and analytics. Analyze revenue, expenses, margins, cash flows, payment volumes, disputes, exceptions, and processing performance. Partner with Finance and Payment Operations to investigate discrepancies, automate workflows, improve data quality, and translate business questions into reliable models and actionable insights.

Designs, develops, administers, and supports WebFOCUS, Power BI, SQL-based reporting, and secure report-delivery platforms. Responsibilities include platform administration, report scheduling, SFTP automation, incident resolution, root-cause analysis, GDPR data purges, SQL data extraction, data-quality assessments, governance support, compliance, documentation, and stakeholder consultation.

Leads advanced data analysis, dashboard and data model development, data quality initiatives, and analytics projects. Partners with stakeholders to define business questions, validate experiments, communicate insights, and support data product lifecycles. Mentors junior analysts and applies quantitative analysis, data mining, governance, and emerging AI-enabled analytics to improve decision-making.

Design, develop, and maintain interactive Power BI/Fabric dashboards, conduct data analysis to surface trends and insights, collaborate with stakeholders to gather requirements, ensure data quality through ETL and modeling, and present findings to inform business decisions.