Associate Analyst, Analytics & Metrics

Overview
We are seeking a Data Analyst to transform complex payments, transaction, customer, and operational data into accurate reporting and actionable business insights. The role combines hands-on data management, data quality, SQL and Python analysis, dashboard development, reporting automation, and stakeholder collaboration. The successful candidate will work with business and technology teams to improve performance, identify trends and growth opportunities, and support reliable, data-driven decisions.
Role
Data Preparation, Quality and Governance
• Extract, compile, cleanse, validate, standardize, and organize large datasets from multiple internal and external sources using SQL, Python, Excel, and approved data platforms.
• Review data for completeness, accuracy, consistency, duplication, and compatibility; investigate discrepancies and correct issues where possible or escalate them through defined channels.
• Research and obtain missing information required to complete datasets, records, or supporting documentation.
• Apply defined data extraction, labelling, categorization, mapping, reconciliation, and quality-control procedures.
• Perform internal quality reviews and maintain clear evidence of checks, corrections, assumptions, and data lineage.
• Handle sensitive and confidential information with professional maturity and comply with applicable data privacy, security, governance, and retention requirements.
Analysis and Business Insights
• Analyze payments and transaction data to identify trends, anomalies, customer behavior, operational performance, and potential growth or efficiency opportunities.
Use descriptive statistics, trend analysis, segmentation, model testing, and root-cause analysis
• Translate business requirements into structured analytical approaches, datasets, metrics, and reporting solutions.
• Interpret findings with the end user in mind and provide clear, practical recommendations supported by data.
• Collaborate with business, operations, product, risk, and technology stakeholders to clarify requirements and validate analytical outputs.
SQL, Python, Databricks and Automation
• Write and optimize SQL queries using joins, common table expressions, subqueries, aggregations, and window functions for extraction, validation, reconciliation, and analysis.
• Use Python, particularly pandas and NumPy, to manipulate data, perform quality checks, conduct analysis, and build reusable processing scripts.
• Work with Databricks notebooks and Spark or PySpark for data preparation and distributed processing, with guidance for more complex engineering activities.
• Automate recurring reports, validation routines, and data preparation processes to reduce manual effort and improve consistency.
• Support basic machine-learning activities such as feature preparation, model testing, performance evaluation, classification, clustering, or predictive analysis under established frameworks.
• Document code, logic, dependencies, business rules, and operating procedures so solutions can be reviewed, maintained, and reused.
Reporting and Visualization
• Build, maintain, and enhance dashboards in Power BI, Tableau, or similar tools to track business metrics, KPIs, trends, and exceptions.
• Prepare accurate recurring and ad-hoc reports, presentations, and analytical summaries for technical and non-technical audiences.
• Validate dashboard calculations and reporting outputs against source systems before release.
• Store completed work in designated repositories, maintain appropriate versions, and perform backup activities as instructed.
• Present findings, limitations, and recommendations clearly to stakeholders and respond to follow-up questions with supporting evidence.
Delivery and Collaboration
• Manage multiple assignments or projects simultaneously while meeting agreed quality, productivity, and turnaround expectations.
• Work effectively in a fast-paced environment, prioritize competing requests, and communicate risks or delays early.
• Partner with cross-functional and global teams, participate in reviews, and contribute to process and reporting improvements.
• Operate independently on defined tasks while seeking appropriate guidance for complex, ambiguous, or high-risk issues.
• Demonstrate professionalism, accountability, punctuality, attention to detail, and a goal-oriented approach.
All About You
Required Qualifications and Experience
• Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Engineering, Data Science, Commerce, or a related discipline.
3-4 years of experience in data analytics, business intelligence, data management, reporting, or a related technical role.
• Proven experience working with large datasets, performing data validation, resolving quality issues, and delivering accurate analytical outputs.
• Strong SQL skills, including joins, aggregations, common table expressions, window functions, and complex queries.
• Proficiency in Python for data manipulation and analysis, particularly pandas and NumPy.
• Good to have knowledge on Power BI, Tableau, or a comparable data-visualization platform.
• Good Microsoft Excel skills, including formulas, pivot tables, lookups, data analysis, and preferably Power Query.
• Working knowledge of statistics, trend analysis, model testing, and analytical problem-solving.
• Excellent written and verbal communication skills, including report writing, presentations, and stakeholder discussions.
• Strong organizational and time-management skills, with the ability to manage priorities and deliver work accurately within deadlines.
• Experience with web-based applications, internet research, Microsoft Office, and common data-management tools.
Preferred Qualifications
• Experience in payments, banking, fintech, financial services, or another transaction-intensive environment.
• Familiarity with payment and transaction metrics, customer segmentation, fraud or anomaly analytics, and operational performance reporting.
• Exposure to Databricks, Spark, PySpark, cloud data platforms, Delta tables, or distributed data-processing concepts.
• Exposure to machine-learning modelling, including data preparation, feature engineering, model evaluation, and interpretation of results.
• Experience with APIs, Git or version control, reporting automation, A/B testing, or predictive analytics.
• Relevant certifications in analytics, business intelligence, cloud platforms, Python, SQL, or Databricks are advantageous.
• Flexibility to support India night shifts, a 24x7 operating model, or alignment with the US holiday calendar where required by the team.
Success in This Role
• Deliver accurate, complete, and timely analyses, reports, and datasets with clear quality checks.
• Create dashboards and visualizations that are easy to understand, relevant to end users, and aligned with agreed business metrics.
• Reduce manual reporting and data-preparation effort through practical, maintainable automation.
• Identify meaningful trends, discrepancies, risks, and opportunities and convert them into actionable recommendations.
• Build stakeholder confidence through reliable delivery, transparent communication, sound documentation, and responsible handling of data.

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;


• Ensure the confidentiality and integrity of the information being accessed;


• Report any suspected information security violation or breach, and


• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.