Mastercard Hiring: Senior Data Analyst 2026 | Gurgaon

Mastercard Hiring: Senior Data Analyst 2026:-

Mastercard Recruitment 2026: Mastercard is hiring a Senior Data Analyst for its Product Data & Analytics team in Gurgaon, Haryana.

Role Overview:-

The Senior Data Analyst – Product Data & Analytics will develop and maintain data engineering solutions, analytical models, reporting systems, dashboards, and visualization tools that support Mastercard’s products, markets, and services.

The role combines advanced SQL, data management, big data technologies, business intelligence, quantitative analysis, and stakeholder engagement.

The analyst will gather information from multiple data sources, analyze large datasets, identify business trends, and translate findings into strategic recommendations. The position also includes mentoring junior colleagues and contributing to the development of analytics talent.

Key focus areas include:

• Large-scale data analysis and data mining
• Advanced SQL and query optimization
• Data engineering and analytical solution development
• Semantic models and self-service BI
• Tableau, Power BI, and QlikView
• Databricks, Microsoft Fabric, and Hadoop technologies
• Python, PySpark, Hive, and R
• Cloud platforms such as AWS and Azure
• Business performance and revenue optimization
• Executive reporting and stakeholder communication
• Data quality, validation, and governance
• Team mentoring and talent development

Educational Requirements:-

Candidates should hold a:

• Bachelor’s degree or Master’s degree in Computer Science.
• Degree in Information Technology.
• Degree in Engineering.
• Degree in Mathematics or Statistics.
• Degree in a related technical or quantitative discipline.

The supplied description does not specify a minimum percentage, graduation year, or mandatory professional certification.

Experience requirement: The description emphasizes strong technical experience across data management, SQL, analytics, reporting, data product development, and big data platforms, but it does not state a specific minimum number of years.

Key Skills:-

Technical Skills

1. SQL and Database Technologies

• Advanced SQL query development
• SQL query optimization for large datasets
• Data extraction, transformation, and analysis
• Large-scale database management
• Data mining and quantitative analysis
• SQL Server
• Data validation and cleansing
• Database performance optimization

2. Business Intelligence and Data Visualization

• Tableau
• Microsoft Power BI
• QlikView
• Advanced reporting and visualization
• Interactive dashboard development
• Performance metrics and KPI reporting
• Semantic data modeling
• Self-service business intelligence
• Executive-ready reports and presentations
• Microsoft Excel and PowerPoint

3. Big Data and Data Engineering

• Databricks
• Microsoft Fabric
• Cloudera Hadoop
• Python
• PySpark
• Hive
• R
• Data engineering and analytical pipelines
• Data integration from multiple sources
• Large-scale data processing
• Data product development

4. Cloud and Microsoft Data Platforms

• Amazon Web Services (AWS)
• Microsoft Azure
• SQL Server Integration Services (SSIS)
• SQL Server Analysis Services (SSAS)
• SQL Server Reporting Services (SSRS)
• Enterprise analytics platforms
• Cloud-based data processing and reporting

Functional Skills

• Data management and data governance
• Business intelligence solution development
• Advanced data analytics
• Quantitative and statistical analysis
• Data product development
• Business performance monitoring
• Product and portfolio analytics
• Revenue optimization analysis
• Strategic business decision support
• Cross-functional project execution
• Requirements gathering
• Data quality management
• Risk mitigation and project management
• Business reporting and insight generation
• Analytical methodology development
• Knowledge sharing and process improvement
• Team leadership and mentoring

Soft Skills

• Strong analytical and problem-solving abilities
• Excellent written and verbal communication
• Attention to detail and data accuracy
• Strategic thinking and business judgment
• Ability to simplify complex analytical findings
• Effective stakeholder management
• Collaboration across technical and business teams
• Creativity and innovation
• Adaptability to new technologies
• Self-motivation and a sense of urgency
• Ability to manage multiple priorities
• Project coordination and risk awareness
• Ability to work in fast-paced environments
• Mentoring and leadership capabilities

Roles & Responsibilities:-

1. Data Engineering and Analytical Solution Development

• Architect, develop, and maintain data engineering solutions that support business analytics and reporting requirements.
• Build scalable analytical capabilities for products, markets, and services.
• Design data solutions that help users access reliable information for business decision-making.
• Work with large datasets and multiple data sources to develop comprehensive analytical outputs.
• Support the development of data products and reusable analytical assets.
• Improve data processing and analytical workflows to support business needs.
• Apply appropriate technologies and methodologies to solve complex data challenges.
• Maintain and enhance existing analytical systems and reporting solutions.

2. Data Extraction, Integration, and Analysis

• Obtain data from multiple internal and external sources as required.
• Collect, collate, analyze, and triangulate information to establish reliable fact bases.
• Use large-scale databases and analytical tools to manipulate and interpret data.
• Identify patterns, trends, anomalies, and relationships within complex datasets.
• Combine information from different sources to develop actionable business insights.
• Evaluate data completeness, consistency, and reliability before drawing conclusions.
• Support cross-functional analytical projects involving multiple business domains.
• Translate analytical findings into recommendations that support business priorities.

3. Advanced SQL and Big Data Processing

• Develop and optimize SQL queries for large-scale database environments.
• Manipulate and analyze complex datasets using advanced querying techniques.
• Work with big data technologies and platforms such as Databricks, Microsoft Fabric, and Cloudera Hadoop.
• Use Python, PySpark, Hive, or R as appropriate for analytical and data-processing tasks.
• Support data extraction, transformation, and integration activities.
• Improve the efficiency and scalability of data processing workflows.
• Investigate data-related issues and help resolve performance or quality concerns.
• Apply appropriate technical approaches to process and analyze large volumes of data.

4. Data Modeling and Business Intelligence

• Build, develop, and maintain data models that support reporting and analytics.
• Develop semantic models and self-service BI solutions for business users.
• Create dashboards, reports, and visualization solutions using Tableau, Power BI, and QlikView.
• Establish reporting structures and performance metrics aligned with business outcomes.
• Design analytical solutions that make data accessible and understandable to end users.
• Support the development of standardized reporting methods and reusable data assets.
• Improve dashboard usability, analytical consistency, and reporting effectiveness.
• Ensure that reports and dashboards provide reliable information for business decisions.

5. Business Performance and Revenue Optimization

• Analyze product, portfolio, market, and service performance.
• Identify opportunities to improve revenue performance and optimize business outcomes.
• Evaluate business trends, performance gaps, and potential growth opportunities.
• Support initiative tracking and new product development through data analysis.
• Contribute to Go-To-Market (GTM) strategy development using analytical insights.
• Assess business performance using relevant metrics and quantitative methods.
• Develop recommendations that support strategic planning and operational improvement.
• Help stakeholders understand the business impact of analytical findings.

6. Data Quality, Validation, and Governance

• Apply data quality controls to maintain accurate and reliable data assets.
• Perform data validation, cleansing, and consistency checks.
• Identify discrepancies, missing information, and anomalies within datasets.
• Investigate the causes of data quality issues and support corrective action.
• Ensure analytical outputs are based on validated and trustworthy data.
• Promote consistent data management practices across projects.
• Maintain appropriate documentation of analytical processes and data methodologies.
• Support the continued improvement of data reliability and governance.

7. Dashboard Reporting and Executive Presentations

• Present analytical findings through Tableau, Power BI, Excel, and PowerPoint.
• Develop clear, insight-driven reports and executive-ready presentation materials.
• Translate technical analysis into meaningful business recommendations.
• Explain performance trends, risks, opportunities, and potential business impacts.
• Adapt communication to different audiences, including technical teams and senior leaders.
• Support stakeholder discussions with reliable data and evidence-based conclusions.
• Prepare reporting materials that help decision-makers evaluate business performance.
• Communicate findings in a concise, structured, and business-focused manner.

8. Cross-Functional Collaboration and Project Execution

• Execute analytical projects in collaboration with technology, sales, marketing, product, and leadership teams.
• Gather and clarify stakeholder requirements.
• Translate business problems into analytical questions and technical solutions.
• Coordinate with cross-functional teams to deliver reporting and data products.
• Manage project priorities, risks, and dependencies as required.
• Exercise sound judgment when working with ambiguous business requirements.
• Ensure analytical deliverables align with business objectives and expected outcomes.
• Share relevant findings and recommendations with stakeholders throughout project execution.

9. Knowledge Sharing and Best Practices

• Capture and share analytical methodologies, tools, and best practices across projects.
• Develop reusable approaches that improve consistency and efficiency.
• Contribute to organizational learning and analytics capability development.
• Document relevant processes, technical solutions, and analytical approaches.
• Share technical knowledge with colleagues and other analytics teams.
• Promote data-driven decision-making through clear communication and knowledge sharing.
• Help establish repeatable analytical practices across business functions.

10. Team Leadership and Talent Development

• Lead, mentor, and guide junior data analysts and team members.
• Support the professional development of analysts and data engineering talent.
• Provide guidance on analytical techniques, reporting standards, and technical approaches.
• Contribute to recruiting, training, and developing data analyst and data engineering talent.
• Support knowledge transfer and collaborative problem-solving.
• Encourage high standards of technical quality, data accuracy, and analytical rigor.
• Help team members navigate complex assignments and develop their capabilities.

About the Company:-

Mastercard is a global payments technology company that connects consumers, businesses, financial institutions, and governments through digital payment networks and related services.

The company operates across more than 200 countries and territories, supporting secure, simple, smart, and accessible payment experiences. Its technology, data, partnerships, and global network help organizations improve payment experiences and make informed business decisions.

Mastercard’s analytics teams use data and technology to understand business performance, optimize products and portfolios, identify revenue opportunities, and support strategic decisions.

Mastercard Hiring: Senior Data Analyst 2026 Application Process:-

Apply Link:- Click Here To Apply

Note:- Only the shortlisted candidates will be notified for the further interview process.

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