Data Quality & Governance Engineer

You will be the lead engineer responsible for embedding Governance-as-Code into the Data Platform. Your mission is to automate the enforcement of data quality, security (DLP), and compliance (Retention/Privacy) policies. You will work closely with the Data Governance team to translate business and legal requirements into automated technical controls within our Lakehouse and BigQuery ecosystem.

The candidate will have responsibilities across the following functions:

Data Privacy and Security (DLP/RLS):

• Automated DLP: Implement Cloud DLP (Data Loss Prevention) to automatically detect, classify, and mask PII/Sensitive data across the Lakehouse.
• Access Control: Build and manage Policy Tags and Row-Level Security (RLS) in BigQuery based on user attributes and data sensitivity.
• WIF and Identity: Integrate Workload Identity Federation (WIF) to ensure secure, keyless authentication for all data workloads.

Data Quality and Validation Frameworks:

• Quality-as-Code: Build a scalable framework for Data Quality Rules (using dbt tests, Great Expectations, or Soda) including dashboards for data health scores and automated remediation.
• Model Validation: Implement automated checks to ensure all new data products align with the Universal Data Model (UDM) and Master Data Management (MDM) consistency rules.

Compliance and Lifecycle Automation:

• Retention and Cleaning: Automate Data Retention Policies (expiration, cold archiving, and permanent deletion) to comply with data protection laws (GDPR/LGPD).
• Metadata and Cataloguing: Integrate platform metadata with Dataplex and the Universal Metadata Catalogue (MFW) to ensure 100% lineage and ownership visibility.
• Discovery: Implement repository scanning to discover hidden data assets and cross-domain dependencies.

Technical Requirements (Must-Have):

• GCP Data Stack: Expert knowledge of BigQuery, Dataplex, Cloud DLP, and IAM/RBAC structures.
• SQL: Expert level. You must be able to build complex validation
• Python: Proficiency in building automation scripts for metadata extraction and lifecycle management.
• Data Governance Tools: Experience with Data Catalogues (Collibra, Atlan, or Dataplex) and Business Glossaries.
• Compliance Knowledge: Practical understanding of Data Privacy laws and how they translate to technical "Hard Deletes" or "Anonymisation. "