US_East | Data Engineer_L2

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. • Build and maintain semantic layers, taxonomies, and ontologies that support natural-language search and AI grounding use cases.

Role Name: Knowledge Context Engineer
Work site: Bernards, NJ 07920 (Onsite)


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