Data Governance Engineer

Description

**REQUIREMENTS** Experience * 3\+ years of experience in data governance, data quality, or data architecture, with a solid technical engineering foundation. This position requires hands-on experience implementing technical controls and automation—not limited to process or documentation tasks. Education * Technical degree-level education: Bachelor's degree in Engineering, Technology Sciences, Mathematics, Economics, Physics, Chemistry, Statistics, or similar. Technical skills * AWS Lake Formation and AWS Glue Data Catalog. * Data cataloging, crawlers, and classifiers. * Apache Spark for batch and streaming processing. * Delta Lake to implement data quality and governance controls. * Confluent Kafka, Schema Registry, data contracts, and topic governance. * AWS Lambda to automate event-driven governance controls and workflows. * Amazon SageMaker and its relationship with data governance for Machine Learning. * Feature stores and dataset traceability. * Knowledge of regulatory frameworks such as GDPR, PCI-DSS, or other applicable standards. * Ability to translate regulatory requirements into technical controls. * Advanced SQL and ability to audit and validate transformations in data pipelines. Additional Information Preferred qualifications: * Experience with cataloging and lineage tools such as DataHub, Collibra, Amundsen, or OpenLineage. * Familiarity with data mesh architectures. * Experience with data domains, data contracts, and data products. * Experience in regulated industries, especially financial services or payment processing. * AWS certifications related to security or data analytics. * We seek a hybrid profile bridging policy and code—rigorous, with strong communication skills to collaborate effectively with Compliance, Legal, Engineering, and Business teams. **KEY RESPONSIBILITIES** * Implement and operate data governance—cataloging, quality, security, compliance, and lineage—directly on the AWS-based engineering platform (Confluent, Glue, Spark, Delta Lake), going beyond purely document-based definitions. Key activities: * Design and implement the access governance model using AWS Lake Formation, including table-, column-, and row-level permissions, Tag-Based Access Control, and IAM integration. * Administer and evolve the AWS Glue Data Catalog, managing schemas, classifications, ownership, and data sensitivity—including PII identification. * Define and implement automated data quality controls over Spark and Delta Lake pipelines, including schema validation, completeness checks, duplicate detection, and anomaly identification. * Integrate quality and governance controls into AWS Glue, Lambda, and EMR. * Design retention policies, versioning, and lineage for Delta Lake tables using time travel, Change Data Feed, and change auditing. * Define, together with Data Engineering, naming conventions, partitioning standards, and data contracts for Kafka topics and Delta Lake tables. * Establish mechanisms for classification and protection of sensitive data—including encryption, masking, and role- or domain-specific access controls. * Provide technical and regulatory support to Data Science and Machine Learning teams for governed access to feature stores and certified datasets. * Audit compliance with controls in pipelines built using Lambda, Glue, and EMR. * Produce data quality and governance metrics and reports—including data quality scorecards, domain catalogs, and lineage health indicators. * Serve as the primary liaison between Engineering, Security, Legal, Compliance, and Business units.