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Job Description
Structured overview of role & requirementsAbout This Role
Lead enterprise Data Quality strategy, architecture, and best practices to enhance data trust and governance across multiple environments including batch, streaming, cloud, on-premises, and SaaS.
Design and develop scalable, metadata-driven, reusable data quality platforms and observability solutions, including rule management, anomaly detection, and remediation workflows.
Partner across business, governance, AI, and engineering teams to embed automated data quality controls and enable Data Quality as Code, leveraging AI and automation to improve operational efficiency and platform adoption.
Minimum Requirements
Bachelor's degree in Computer Science, Information Systems, Engineering, or related discipline.
Minimum 10 years experience in Data Engineering, Data Platforms, or Data Architecture.
At least 5 years experience leading enterprise-scale data quality, observability, or data reliability initiatives.
Hands-on expertise in SQL, Python, Spark/PySpark, streaming technologies, cloud data platforms, and strong understanding of metadata management, data governance, data contracts, and lineage.
Ideal Candidate Profile
Experienced leader in designing large-scale distributed data systems and platform services with deep expertise in Data Warehousing, Lakehouse architectures, and metadata-driven frameworks.
Proven ability to drive enterprise-wide adoption of data quality practices, integrating data quality as a product and code, with strong executive communication and stakeholder management skills.
Demonstrated experience applying AI, Machine Learning, or Generative AI technologies to enhance data quality monitoring, anomaly detection, remediation, and support AI-ready data platforms.