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
Principal Data Engineer - Data Quality
Overview
We are seeking a visionary and technically strong engineering leader to drive the evolution of our Enterprise Data Quality capabilities and platforms. The Principal Data Engineer, Data Quality will lead the design, development, and adoption of scalable data quality frameworks, services, and engineering practices that improve trust, reliability, observability, and governance of data across the organization. This role requires a strategic thinker with deep expertise in data engineering, data quality, metadata management, and platform architecture, capable of influencing enterprise-wide technology decisions and partnering closely with engineering, governance, analytics, AI, and business teams to deliver innovative and scalable solutions. The successful candidate will lead the development of reusable capabilities that enable consistent, automated, and measurable data quality controls across diverse data ecosystems, including batch, streaming, cloud, on-premises, and SaaS environments. The ideal candidate combines strong technical depth with business acumen, executive presence, and organizational leadership. They will be responsible for balancing immediate business needs with long-term platform strategy, promoting Data Quality as a Product and Data Quality as Code mindset, while leveraging automation, observability, and emerging AI technologies to improve data trust, operational efficiency, and enterprise-wide adoption.
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Role
- Define and drive the enterprise Data Quality strategy, roadmap, architecture, standards, and best practices.
- Lead the design, development, and evolution of scalable data quality platforms, frameworks, and shared services supporting batch, streaming, cloud, on-premises, and SaaS environments.
- Architect and govern metadata-driven, reusable, and configurable data quality capabilities, including rule management, observability, profiling, anomaly detection, and remediation workflows.
- Establish enterprise processes for defining, versioning, deploying, and monitoring technical and business data quality rules.
- Design and implement Data Quality observability solutions, including trust scores, quality metrics, SLAs, dashboards, alerts, and executive reporting.
- Partner with Data Governance, Data Management, Security, Compliance, and business stakeholders to operationalize quality controls and stewardship processes.
- Drive adoption of data quality practices across the data product lifecycle, promoting shift-left quality engineering and Data Quality as Code principles.
- Ensure quality frameworks are integrated with metadata management, lineage, catalog, governance, MDM, and data contract capabilities.
- Provide technical leadership, architecture guidance, and mentoring to engineering teams across multiple programs and domains.
- Lead evaluation, selection, and implementation of data quality tools, platforms, and emerging technologies.
- Collaborate with Analytics, Data Science, and AI teams to ensure trusted, high-quality, AI-ready datasets and data products.
- Identify opportunities to leverage AI, machine learning, and automation to improve data quality monitoring, root cause analysis, remediation, and engineering productivity.
- Establish engineering standards, operational controls, and CI/CD practices to improve platform reliability, scalability, security, and maintainability.
- Define and measure key performance indicators to track quality improvements, platform adoption, operational efficiency, and business outcomes.
- Influence enterprise-wide technology strategy and champion a culture of data quality, ownership, accountability, and continuous improvement.
All About You
- Bachelor’s degree in computer science, Information Systems, Engineering, or related discipline.
- 10+ years of experience in Data Engineering, Data Platforms, or Data Architecture.
- 5+ years leading enterprise-scale data quality, observability, or data reliability initiatives.
- Deep expertise in Data Warehousing, Lakehouse architectures, Metadata management, Data governance and Data quality frameworks
- Hands-on experience with SQL, Python, Spark/PySpark, Streaming technologies and Cloud data platforms
- Strong understanding of Data contracts, Lineage, Master Data Management, Metadata-driven architectures and Data observability
- Experience designing large-scale distributed systems and platform services.
- Excellent stakeholder management and executive communication skills.
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- Experience leveraging AI, Machine Learning, or Generative AI technologies to enhance data quality, observability, engineering productivity, and operational efficiency.
- Familiarity with AI-assisted development tools, intelligent anomaly detection, automated root cause analysis, and metadata enrichment capabilities.
- Experience supporting AI-ready data platforms, including governance, lineage, trust, and quality controls required for AI and GenAI initiatives.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Abide by Mastercard’s security policies and practices;
Ensure the confidentiality and integrity of the information being accessed;
Report any suspected information security violation or breach, and
Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
