Data Scientist II – Analytics & Data Engineering

About the role

Mastercard is seeking a Data Scientist II to join its Global Analytics team. You will help build large‑scale analytical capabilities that support users across six continents, turning data assets into strategic value for the organization.

Key responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for enterprise data and analytics initiatives.
• Build and optimise batch and real‑time data processing solutions using SQL, Python, Spark and related technologies.
• Ensure data availability, accuracy, consistency, and reliability across platforms and pipelines.
• Implement data‑quality controls, testing frameworks, monitoring and observability practices.
• Apply engineering best practices such as version control, CI/CD, DataOps and automated deployment.
• Support platform upgrades, migrations, operational maintenance and production support.
• Collaborate with data engineers, data scientists, analysts and business stakeholders to translate requirements into scalable solutions.
• Participate in technical design reviews, code reviews and architecture discussions.
• Create and maintain technical documentation, standards and operational procedures.

Required profile

• 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 modelling, database design and performance optimisation.
• Experience designing and developing ETL/ELT solutions on modern data platforms.
• Knowledge of data‑quality frameworks, testing methodologies and validation practices.
• Familiarity with cloud‑based or modern enterprise data platforms.
• Experience with source control, CI/CD pipelines and software‑engineering best practices.
• Excellent analytical, problem‑solving and communication skills.

Required skills

• SQL
• Python
• Apache Spark / PySpark
• ETL/ELT development
• Data modelling and database design
• Data‑quality frameworks
• Version control (e.g., Git)
• CI/CD pipelines