Data Analyst

Mastercard is seeking a Data Analyst to join its Global Analytics team. The successful candidate will design, develop, and maintain scalable ETL/ELT pipelines supporting enterprise data and analytics initiatives.

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines supporting enterprise data and analytics initiatives.
• Build and optimize batch and real-time data processing solutions using SQL, Python, Spark, and related technologies.
• Ensure data availability, accuracy, consistency, and reliability across data platforms and pipelines.
• Implement data quality controls, testing frameworks, monitoring, and observability practices to support trusted data products.
• Apply engineering best practices including version control, CI/CD, DataOps, and automated deployment methodologies.
• Support platform upgrades, migrations, operational maintenance, and ongoing production support activities.
• Collaborate with Data Engineers, Data Scientists, Analysts, and business stakeholders to translate business requirements into scalable data solutions.
• Participate in technical design reviews, code reviews, and architecture discussions to drive engineering excellence.
• Create and maintain technical documentation, standards, and operational procedures.

Requirements:

• Hands-on experience in data engineering, data integration, and large-scale data processing.
• Strong proficiency in SQL and Python for data transformation, automation, and analytics workloads.
• Solid understanding of data modeling, database design, and performance optimization techniques.
• Experience designing and developing ETL/ELT solutions across modern data platforms.
• Knowledge of data quality frameworks, testing methodologies, and data validation practices.
• Familiarity with cloud-based or modern enterprise data platforms.
• Experience with source control, CI/CD pipelines, and software engineering best practices.
• Strong analytical and problem-solving skills with the ability to troubleshoot complex data issues.
• Excellent communication and collaboration skills with the ability to work effectively across technical and business teams.
• Self-motivated, detail-oriented, and committed to delivering high-quality, scalable solutions.

Preferred Qualifications:

• Experience with PySpark and Apache Spark for distributed data processing.
• Experience working within the Hadoop ecosystem and large-scale data environments.
• Familiarity with GitLab, Jenkins, or similar DevOps and CI/CD platforms.
• Exposure to Power BI or other business intelligence and data visualization tools.
• Understanding of modern data architecture patterns supporting analytics, machine learning, and AI workloads.