Principal Data Engineer - Data Quality
Mastercard1 month ago
Pune, IndiaStaff+
Responsibilities
- Define and drive 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.
- Architect metadata-driven capabilities for rule management, observability, profiling, anomaly detection, and remediation workflows.
- Establish processes for defining, versioning, deploying, and monitoring technical and business data quality rules.
- Design data quality observability solutions with trust scores, metrics, SLAs, dashboards, alerts, and executive reporting.
- Partner with Data Governance, Data Management, Security, Compliance, analytics, AI, and business stakeholders.
- Promote shift-left quality engineering and Data Quality as Code across the data product lifecycle.
- Integrate quality frameworks with metadata management, lineage, catalog, governance, MDM, and data contracts.
- Provide architecture guidance, technical leadership, and mentoring to engineering teams.
- Evaluate and implement data quality tools, platforms, AI, machine learning, and automation capabilities.
- Establish engineering standards and operational controls that improve platform reliability, scalability, security, and maintainability.
- Define KPIs for quality improvements, platform adoption, operational efficiency, and business outcomes.
Requirements
- Bachelor’s degree in computer science, Information Systems, Engineering, or a 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.
- Experience using AI, machine learning, or generative AI to improve data quality, observability, engineering productivity, or operational efficiency.
- Familiarity with AI-assisted development tools, intelligent anomaly detection, automated root cause analysis, and metadata enrichment.
- Experience supporting AI-ready data platforms with governance, lineage, trust, and quality controls.
- Ability to identify and apply AI-driven solutions to complex data engineering and data quality challenges.
Tech Stack
Categories
Data Engineering
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).