Data Engineer-II (SQL, MSBI, PowerBI, ETL, Reporting, Java/Python)

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 Data Engineer-II (SQL, MSBI, PowerBI, ETL, Reporting, Java/Python) As an Data Engineer-II, you will build, and operate reliable, scalable data pipelines and data products that power analytics, reporting, and downstream data consumers. You will work on well-scoped components while collaborating closely work within your team, product, platform, and stakeholder teams to deliver high-quality, governed datasets and improve data accessibility across the organization. In this role, you will: • Engineer data pipelines end-to-end (batch and/or streaming) that move data from source systems to curated stores (e.g., lake/warehouse), ensuring correctness, performance, and maintainability. • Develop data transformations and data models that produce analytics-ready datasets, choosing appropriate formats/structures and ensuring consistent definitions and lineage-friendly patterns. • Implement data quality and observability (validation checks, reconciliations, monitoring, alerting) to detect issues early and improve trust in data products. • Follow software engineering standards in the data space: version control, code review, automated testing, CI/CD, and disciplined release practices for pipelines and data assets. • Collaborate with stakeholders (product, analysts, data consumers, platform teams) to translate requirements into durable pipelines and reusable datasets; communicate trade-offs and progress clearly. • Apply AI to improve data pipeline testing and release confidence by leveraging AI-driven approaches for generating and validating production-like test data and running automated data quality checks as part of ETL/pipeline validation. • Drive automation and continuous improvement across ingestion, data movement, and access workflows—proactively identifying opportunities to streamline and standardize. • Support production operations including incident response, root cause analysis, and preventive fixes; contribute to runbooks and operational readiness for data services. You will also leverage AI capabilities to streamline repetitive engineering work (e.g., code generation, documentation support, and test data automation) while adhering to Mastercard’s AI governance and security controls. All About You You are a hands-on data engineer with strong fundamentals in building production-grade data systems and a bias for reliability, quality, and automation. You bring a software-engineering mindset to data pipelines and enjoy partnering with others to deliver trusted, reusable data assets. Technical skills • Overall career experience of 2-5 years into Data Engineering • Experience in building and maintaining data pipelines for analytics/reporting use cases, including ingestion, transformation, and curated dataset publishing. • Experience in writing complex SQL queries and exposure in at least one programming language commonly used in data engineering (e.g., Python/Scala/Java), with the ability to write maintainable, testable code. • Practical knowledge of big data ecosystems (e.g., distributed processing patterns, job orchestration concepts, metadata/format considerations) and how to troubleshoot performance and data correctness issues. • Awareness of implementing data quality controls, reconciliation patterns, and operational monitoring to ensure data is ready for use and remains trustworthy over time. • Familiarity with engineering standards applied to data work (source control, peer review, CI/CD, documentation discipline) ... (truncated, view full listing at source)