Knowledge Context Engineer

Role Name: Knowledge Context Engineer

Work site: Bernards Township NJ  (onsite)

Start date: Immediate availability.         

Background check MANDATORY

Request ID: GSTV5H

Key Responsibilities:

·         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.

Required Qualifications:

·         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