Top Data Analyst Jobs in Gurgaon

Analyze complex healthcare data to identify trends, drivers, and optimization opportunities across cost, quality, and growth. Translate ambiguous business questions into analytical approaches, quantify impact, and deliver decision-ready recommendations. Partner with cross-functional stakeholders, data engineering, and platform teams to ensure data quality and availability. Develop analytical assets, communicate findings to technical and executive audiences, and promote evidence-based decision-making.

Analyze company-wide revenue and operational metrics, develop recurring consumption and revenue reports, conduct root-cause analyses, and provide executive-level insights. The role partners with Analytics, FP&A, Accounting, Cloud Billing, and Sales Operations; maintains data pipelines and visualizations; resolves data quality issues; and documents metric definitions, reporting logic, and data lineage. It requires strong SQL, Python, BI, and automation skills, with experience owning business metrics reporting.

Develops advanced analytics solutions to address complex business challenges, using data visualization to communicate insights and influence decisions. Partners with stakeholders to define opportunities, improve and automate processes, and deliver commercial and customer value. Leads colleagues, coordinates data analytics resources, supports strategic projects and scrum outcomes, and connects data science, data engineering, and analytics work. Requires strong data management, data architecture, coding, cloud, visualization, and analytics expertise.

Analyze large datasets to identify trends, patterns, data-quality issues, and business opportunities. Build data pipelines, logical data models, forecasts, simulations, dashboards, and automated reports. Translate analytical findings into recommendations that improve operations and decision-making. The role involves collaborating with technical and business stakeholders, supporting automation and AI adoption, managing controls and risks, and delivering initiatives through agile workflows.

Develops advanced analytics and data solutions to address business challenges, improve processes, and generate customer and commercial value. The role consults with stakeholders, defines analytical questions, creates meaningful visualizations, communicates insights, and coordinates multidisciplinary Agile data teams. It requires technical capability, cloud experience, data architecture knowledge, analytics tooling and coding knowledge, plus an understanding of financial services.

Leads a data and analytics team at vice president level, coaching colleagues and delivering strategic analytics solutions. Partners with stakeholders to define complex business questions, plans analytics resources, applies advanced analytics and visualization, and communicates insights to support business decisions. The role also requires knowledge of data architecture, cloud environments, coding, emerging tools, and methodologies, with a focus on customer and commercial impact.

Develops advanced analytics solutions to address complex business challenges and improve understanding of customers, processes, channels, and products. Consults with stakeholders, translates insights into visualizations, leads strategic projects and scrum outcomes, coaches colleagues, and connects data science and engineering work to business needs. Requires strong technical capability, cloud experience, data architecture knowledge, and the ability to communicate complex findings clearly.

Support EMEA Distribution by transforming client data, delivering commercial insights, building stakeholder relationships, driving data governance, automating processes, and developing AI-enabled analytics to improve decision-making and productivity.

Support EMEA Distribution by transforming and governing client data, delivering analytics and AI/automation initiatives, generating commercial insights, forecasting, and engaging stakeholders to prioritise and implement high-impact data solutions.

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.

Build and manage financial data models, pipelines, Power BI dashboards, DAX measures, and automated reporting solutions using Microsoft Fabric and Dynamics 365. Support FP&A through KPI tracking, monthly reporting, scenario analysis, and decision support. Collaborate with finance, IT, and data teams to ensure data quality, governance, documentation, and a trusted source of financial information for business stakeholders.

Build and optimize procurement data pipelines, ETL/ELT workflows, data models, curated datasets, dashboards, APIs, and automation. Partner with stakeholders to translate analytics needs into technical requirements and deliver end-to-end data products. Implement data quality, governance, documentation, and production support practices while improving solution performance, reliability, and cost. Collaborate on architecture, code reviews, technical standards, and continuous improvements across the data platform.

Build and optimize cloud-based data pipelines, ETL/ELT workflows, data models, curated datasets, APIs, and automation for procurement analytics. Partner with stakeholders to define requirements, develop reporting solutions, monitor data quality, support governance, and improve the performance, reliability, and cost of production data systems.

Manage and maintain accurate static, pricing, reference, and real-time market data across multiple asset classes. Research securities data, support client and stakeholder queries within SLAs, perform maintenance activities, and contribute to systems projects and enhancements. The role requires capital markets and banking knowledge, familiarity with market data vendors and exchanges, analytical problem-solving, and flexibility for rotational shifts.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.

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.

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.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality evaluation, data discoverability, testing, feature releases, customer-focused insights, and quality standards. Uses scientific expertise, data analysis, scripting, databases, cloud platforms, and AI-enabled testing workflows to improve platform reliability and relevance. Coordinates stakeholders, advances projects, documents work, and presents recommendations for product and data improvements.

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.

Analyze large healthcare datasets to extract insights, build data models, reports and dashboards, ensure data quality, collaborate with cross-functional teams, and present findings to stakeholders.

Leads data analytics, engineering, reporting, visualization, and modeling initiatives across products, markets, and services. Builds dashboards, semantic models, performance metrics, and data quality controls using large-scale data technologies. Delivers actionable insights and executive-ready recommendations to cross-functional stakeholders, leads strategic analytical projects, and mentors or supervises junior analysts and data engineering talent.

Support EMEA Distribution through advanced analytics, dashboarding, reporting, automation, forecasting, and trend analysis. Partner with stakeholders to translate complex data into actionable commercial insights, improve client engagement, and enhance business performance. Build scalable Power BI or Tableau solutions, use Python and SQL for analysis and automation, and contribute to continuous process improvement.

Supports EMEA Distribution through advanced analytics, reporting, dashboard development, automation, forecasting, and business diagnostics. Partners with stakeholders to transform complex data into actionable commercial insights, improve client engagement, enhance productivity, and support strategic and operational decisions. The role requires strong Python, SQL, and BI platform skills, clear communication, analytical rigor, and effective problem-solving.