Director of Data and AI

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.