[Hiring] Google Knowledge Catalog Engineer @Capgemini

Role 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.

• Design, configure, and maintain catalog structures (entries, aspect types, tags, glossaries) within Google Cloud's 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.

Qualifications

• 5+ years of experience in data engineering, data governance, or metadata management.
• Hands-on experience with Google Cloud Platform, including BigQuery 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.

Requirements

• 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.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade.
• Company paid holidays.
• Personal Days.
• Sick Leave.
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada).
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada).
• Life and disability insurance.
• Employee assistance programs.
• Other benefits as provided by local policy and eligibility.