Google – Catalog -Engineer

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USA – Local

We are looking for a Google Knowledge Catalog Engineer to build, curate, and maintain an enterprise-wide data and context catalog on Google Cloud. This role owns the discoverability, quality, and semantic richness of data and AI assets – ensuring that technical metadata, business context, and lineage are accurate, enriched, and usable by both human analysts and AI/agentic systems (e.g., RAG pipelines, Gemini-based agents, BI

• Design, configure, and maintain catalog structures (entries, aspect types, tags, glossaries) within Google Cloud’s Knowledge Catalog (built on Dataplex) to organize data across Big Query, Cloud Storage, Spanner, Vertex AI, Pub/Sub, and third‑party sources.
• Curate and enrich metadata – business definitions, ownership, sensitivity classifications, lineage, and usage context – so data assets are discoverable and trustworthy.
• Build and maintain semantic layers, taxonomies, and ontologies that support natural‑language search and AI grounding use cases.
• Automate metadata ingestion and tagging pipelines (e.g., via Dataplex APIs, Cloud Functions, or Dataform) to keep the catalog continuously up to date.
• Partner with data engineering, data governance, and business stakeholders to define and enforce cataloging standards, naming conventions, and data classification policies.
• Support data quality and profiling initiatives, publishing scan results as catalog metadata.
• Monitor catalog health, resolve metadata gaps or inconsistencies, and drive adoption across data and AI teams.
• Contribute catalog context to downstream AI/agentic use cases (e.g., enabling Gemini or MCP‑based agents to query curated metadata).
• Document processes and provide training/enablement to data stewards and business users on catalog usage.

• 5 years of experience in data engineering, data governance, or metadata management.
• Hands‑on experience with Google Cloud Platform, including Big Query and Google Cloud’s Knowledge Catalog / Dataplex (or prior Data Catalog).
• Strong SQL skills; working knowledge of Python for automation/scripting.
• Familiarity with metadata management concepts: lineage, data classification, business glossaries, taxonomy/ontology design.
• Understanding of data governance and compliance principles (access controls, sensitive data handling).
• Experience with APIs and automation tooling for metadata ingestion.

• Experience with Gemini Ent. AI /Vertex AI, RAG architectures, or grounding AI agents in curated enterprise metadata.
• Familiarity with Dataform, Cloud Composer/Airflow, or similar orchestration tools.
• Exposure to data mesh or federated governance models.
• Google Cloud certification (e.g., Professional Data Engineer, Professional Cloud Architect).
• Prior consulting or client‑facing experience.

• Bachelor’s degree in Computer Science, Information Technology, Data Science.

• Strong attention to detail and a structured, systematic approach to organizing information.
• Ability to translate technical metadata into business‑friendly context.
• Collaborative mindset – comfortable working across data engineering, governance, and business teams.
• Clear written and verbal communication for documentation and stakeholder enablement.