Data Governance Engineer- CRIBL required - W2 ONLY!

You will serve as a key individual contributor responsible for building the technical foundation of a formal cyber data governance framework. You will work closely with cyber security, security operations center (SOC), compliance, analytics and platform engineering teams to translate governance policies into implementable metadata, taxonomy, lineage and lifecycle capabilities. Your work will enable teams to reliably discover, trust and use security data to manage risk and drive informed decisions.

What You'll Do

• Implement the technical components of a formal data governance framework, including metadata schemas, policy-to-code patterns and architecture blueprints for cyber data.
• Run domain discovery workshops to define priority cyber data domains, data owners and critical data elements (CDEs), and capture resulting artifacts in the metadata catalog.
• Inventory and rationalize security reports and dashboards, mapping each dashboard to source datasets, accountable owners and key performance indicators (KPIs).
• Enable analytics pilots by designing and delivering curated, well-documented datasets for security and compliance analyst consumption.
• Integrate and automate metadata harvesting from Google BigQuery into a metadata catalog, and onboard Talend, Axonius and Bindplane as governed telemetry and asset sources.
• Define canonical taxonomies and dataset contracts for core cyber concepts such as “asset,” “identity,” “event” and “alert” to drive consistency across platforms.
• Provide end-to-end lineage and provenance for analytics artifacts and model features to support model operationalization and governance requirements.
• Support self-service business intelligence (BI) adoption by publishing dataset contracts, access patterns and technical documentation for Power BI and Looker.

Experience You'll Need To Have

• 6+ years of experience in data architecture, metadata and lineage engineering or data engineering, including implementing governance solutions for security or telemetry data using cloud data platforms.
• 4+ years of experience designing and implementing solutions on Google Cloud Platform, including Google BigQuery and Google Cloud data and security products such as Data Catalog, Identity and Access Management (IAM), Cloud Data Loss Prevention (Cloud DLP) and Security Command Center, Cribl for Data Fabric
• 4+ years of experience integrating and managing data pipelines using Talend or similar integration tools, including onboarding telemetry and asset sources such as Axonius and Bindplane into governed data environments.
• 4+ years of experience in data modeling and SQL, including building and maintaining lineage and automated metadata harvesting solutions across complex data ecosystems.
• 3+ years of experience inventorying and rationalizing reports and dashboards, mapping KPIs to source data and standardizing reporting across business or security domains.
• Demonstrated ability to communicate complex data governance and architecture concepts clearly and effectively with cyber security, analytics and platform stakeholders, including leading working sessions and producing actionable documentation.
• Bachelor’s degree or higher in Computer Science, Information Systems, Engineering, Cybersecurity, Data Analytics or Data Fabric w/ Cribl, or equivalent combination of education, related experience and/or military experience.

Experience That Would Be Great To Have

• Experience working in cybersecurity domains and/or within financial services or other highly regulated industries.
• Professional certifications such as Google Cloud Platform (e.g., Professional Data Engineer, Professional Cloud Architect) or Certified Data Management Professional (CDMP), or equivalent certifications.
• Experience supporting Power BI and Looker consumption patterns, including semantic modeling, dataset governance and role-based access for self-service analytics.
• Experience supporting machine learning model operationalization, including feature store governance, model input lineage and monitoring of governed model outputs.