Software Engineer II

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 Software Engineer II OVERVIEW: Mastercard is the global technology company behind the world's fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. As a Data Quality Engineer in Data Platform & Engineering Services, you will have the opportunity to build high performance data pipelines to load into Mastercard Data Warehouse. Our Data Warehouse provides analytical capabilities to number of business users who help different customer provide answer to business problems through data. You will play a vital role within a rapidly growing organization, while working closely with experienced and driven engineers to solve challenging problems. Key Responsibilities: Build and enhance data quality validation frameworks for large‑scale financial and card‑processing systems. Design, implement, and maintain automated data quality pipelines using Python, Spark, and SQL. Develop and integrate APIs that support data validation workflows, metadata services, and quality scoring. Build real‑time streaming data quality checks using Kafka/Spark Streaming for high‑velocity transaction data. Implement automated rules to validate data completeness, accuracy, timeliness, lineage, and business logic. Collaborate with platform, product, and analytics teams to ensure end‑to‑end data reliability. Contribute to CI/CD, monitoring, alerting, and observability for data quality systems. Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related technical field. Software engineering experience building data services or pipelines, with strong proficiency in Python (frameworks, packaging, testing, CI). Hands‑on experience with Apache Spark (batch and/or streaming) for high‑volume processing, including performance tuning and job orchestration. Advanced SQL skills for implementing validation logic and reconciliations in code (CTEs, windowing, partitioning), with a focus on query performance and reliability. Experience designing and consuming RESTful APIs for data quality services (rules management, metrics, metadata) and integrating them into applications/pipelines. Practical expertise with streaming platforms (Kafka, Kinesis, or Spark Structured Streaming) to enforce real‑time data quality checks and SLAs. Hands‑on with AWS (S3, Lambda, Glue, EMR, Step Functions, ECS/EKS, CloudWatch) and modern data lake/warehouse patterns; infrastructure‑as‑code experience is a plus (CDK/Terraform). Deep understanding of data quality dimensions (accuracy, completeness, timeliness, consistency, validity) and the ability to translate business rules into automated validation code and tests. Strong engineering practices: unit/integration testing, observability (logs/metrics/traces), performance optimization, secure coding, and CI/CD pipelines (GitHub Actions/Jenkins). Excellent problem‑solving and communication skills; proven ability to collaborate with platform, product, and data teams in an agile environment. Preferred / Good to Have Exposure to AI/ML for anomaly detection, rule inference, or synthetic data generation to enhance autom ... (truncated, view full listing at source)