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Specialized data-quality domain but common cloud tools and mid-level seniority imply moderate competition.
Data-platform and governance skills are transferable but entail moderate domain specificity.
Domain expertise and mandatory cloud/data tooling make screening moderately strict.
Lead design and continuous enhancement of the Enterprise Data Quality Framework across data ecosystems to ensure data trustworthiness and readiness for AI applications.
Establish and operationalize automated data quality capabilities including profiling, validation, monitoring, and issue management with measurable KPIs and SLAs.
Collaborate with Data Engineering, Product Management, and cross-functional teams to embed Data-as-a-Product quality practices throughout the data lifecycle.
Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, Information Systems, or related field required; Master's preferred.
Intermediate level relevant work experience required (exact years not specified).
Hands-on experience with enterprise Data Quality programs, including profiling, validation, reconciliation, root cause analysis, and monitoring across large-scale data environments.
Experience with modern cloud data platforms and technologies such as Snowflake, Databricks, SQL, Matillion, dbt, and Power BI.
Experienced in implementing and automating data quality controls and governance frameworks across end-to-end data lifecycles in large enterprises.
Proficient in coordinating with cross-functional teams and embedding scalable data quality and governance standards into enterprise pipelines and products.
Familiar with Data Quality, Data Governance, Metadata Management, Lineage, Stewardship, Master Data Management, and Data-as-a-Product methodologies and tools.