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Specialized data-quality role with metro location and broad platform requirements leads to moderate competition.
Data governance and platform skills transfer across industries but require domain governance knowledge, yielding moderate sensitivity.
Mandatory domain expertise and specific platform experience (Snowflake, Databricks, dbt, etc.) imply strict screening.
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Lead design, implementation, and evolution of the Enterprise Data Quality Framework across data ecosystems including ingestion, warehouses, semantic layers, and Data Products.
Establish scalable data quality standards, KPIs, automated profiling, validation, monitoring, and governance processes to ensure trusted, certified, and AI-ready enterprise data.
Collaborate with Data Engineering, Product Management, and business teams to embed Data Quality practices across the data lifecycle, including rule management, metadata standards, lineage validation, and remediation of issues.
Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, Information Systems, or related field required; Master's degree preferred.
Intermediate level of relevant work experience required; exact years not specified.
Hands-on experience with enterprise Data Quality programs, cloud data platforms (Snowflake, Databricks, SQL, Matillion, dbt, Power BI), and Data Quality/Governance tools.
Not explicitly mentioned: Notice period, mandatory location, or regulatory compliance details except potential licensing for export controls.
Experienced in managing enterprise-scale Data Quality frameworks spanning Data Warehouse, Lakehouse, and Data Products with strong operational control focus.
Technically adept with cloud data platforms and automation of data quality controls across end-to-end data pipelines.
Able to engage cross-functional teams including Data Engineering and Business to integrate Data-as-a-Product quality standards and governance throughout data lifecycle.