Knowledge Context Engineer

· Design, configure, and maintain catalog structures (entries, aspect types, tags, glossaries) 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.

· 3+ years of experience in data engineering, data governance, or metadata management.

· 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.

· Preferred Qualifications

· 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.

· Prior consulting or client-facing experience.

· 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.

· Ingest metadata from knowledge sources

· Curation of Knowledge Assets - Business glossaries, tagging, aspect definition

· Build workflows to review and approve knowledge assets with key stakeholders

Identify knowledge gaps and mitigation