Description
Mastercard is seeking a visionary and technically strong engineering leader to drive the evolution of its 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.
Key responsibilities:
- 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.
Requirements:
- 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.