Associate Analyst, Analytics & Metrics

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary 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