About the Company
JOB DESCRIPTION As the Director of Data & AI Engineering, you will lead the foundation of our Intelligence Stack — driving metadata management, data cataloging, and next-generation data pipelines that fuel our multi-agent AI platforms and Trust Layer architecture.
About the Role
As the Director of Data & AI Engineering, you will lead the foundation of our Intelligence Stack — driving metadata management, data cataloging, and next-generation data pipelines that fuel our multi-agent AI platforms and Trust Layer architecture.
Responsibilities
- Own the Data Foundation : Lead initiatives around metadata management, data cataloging, lineage, and governance to make data discoverable, trustworthy, and reusable across AI systems.
- Architect Scalable Data & AI Infrastructure : Design unified data and model pipelines integrating ingestion, transformation, RAG, and inference layers.
- Lead High-Performing Teams : Build and mentor cross-functional teams across data engineering, AI engineering, and MLOps, setting standards for performance, observability, and automation.
- Operationalize AI with Data : Develop data services and vectorized pipelines that continuously enrich AI reasoning, retrieval, and contextual grounding.
- Integrate Across Ecosystem : Collaborate with Product, Infra, and Cloud teams to embed AI and data layers into enterprise applications, analytics, and automation workflows.
- Govern with Trust : Define and implement guardrails for data quality, lineage, bias detection, and model compliance — aligning to our AI safety and trust principles.
- Shape Data & AI Strategy : Evolve ProductSquads’ enterprise data fabric to power multi-tenant AI agents and real-time decision systems.
Qualifications
Experience building centralized metadata and data catalog systems (e.g., Amundsen, DataHub, Open Metadata).Hands-on expertise with vector databases (Pinecone, Milvus, FAISS) and contextual retrieval architectures.Understanding of knowledge graphs, semantic search, and data lineage frameworks.Proven record of embedding AI + data systems into enterprise-scale applications.Passion for leading data-driven innovation and mentoring teams that bridge data engineering with applied AI.Required Skills
Experience building centralized metadata and data catalog systems (e.g., Amundsen, DataHub, Open Metadata).Hands-on expertise with vector databases (Pinecone, Milvus, FAISS) and contextual retrieval architectures.Understanding of knowledge graphs, semantic search, and data lineage frameworks.Proven record of embedding AI + data systems into enterprise-scale applications.Passion for leading data-driven innovation and mentoring teams that bridge data engineering with applied AI.Preferred Skills
Experience building centralized metadata and data catalog systems (e.g., Amundsen, DataHub, Open Metadata).Hands-on expertise with vector databases (Pinecone, Milvus, FAISS) and contextual retrieval architectures.Understanding of knowledge graphs, semantic search, and data lineage frameworks.Proven record of embedding AI + data systems into enterprise-scale applications.Passion for leading data-driven innovation and mentoring teams that bridge data engineering with applied AI.