> Markdown version of [/jobs/ext/3959243-google-knowledge-catalog-engineer](https://www.wearedevelopers.com/jobs/ext/3959243-google-knowledge-catalog-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Google - Knowledge Catalog -Engineer

- **Company:** Capgemini
- **Location:** Calera, AL, United States
- **Experience:** Expert
- **Salary:** $101,180.0 - $182,624.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, BigQuery, Cloud Computing, Cloud Storage, Information Engineering, Data Governance, Database Queries, Python (Programming Language), Metadata, Meta-Data Management, Data Mesh, Workflow Management Systems, Data Processing, Scripting, Google Cloud, Data Classification, Ontology Design, Retrieval-Augmented Generation, Agentic-AI, Data Layers, Information Technology, Data Management, Google Gemini, Programming Languages
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/google-knowledge-catalog-engineer-calera-al--83d16c4c-35d3-4381-8dc1-4e1bccba8ca0

## About the Role

Team Player, Cloud Computing, Metadata, Business Intelligence, Artificial Intelligence (AI), Cloud Storage, Ontology, Use Cases, Taxonomies, Artificial Intelligence (AI) Programming Languages, * 5+ years of experience in data engineering, data governance, or metadata management.

 * Hands-on experience with Google Cloud Platform, including BigQuery and Google Clouds 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.

Educations :

 * Bachelors degree in Computer Science, Information Technology, Data Science.

Soft Skills

 * 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.

## Description

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

Your Role and Responsibility

 * Design, configure, and maintain catalog structures (entries, aspect types, tags, glossaries) within Google Clouds Knowledge Catalog (built on Dataplex) to organize data across BigQuery, 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.