Director, Enterprise AI Data

Boomi

9h•$195,000 - $268,000•Hybrid

About The Position

Boomi is building the AI data foundation that powers the next generation of enterprise agents — that help our teams move faster and serve customers better. As the Director of Enterprise AI Data, you will lead the team that builds that foundation. You will be a player-coach who reviews architecture as easily as you set strategy, partner with the Director of AI on the data/agent handoff, and report directly to the VP of AI.

Requirements

10+ years in data engineering or data platform, with 3+ years managing senior IC engineers.
Has personally architected at least one of: a production knowledge graph, a semantic layer, or a RAG retrieval system at enterprise scale — and is willing to stay close to the code as a player-coach.
Hands-on AWS depth: IAM, S3, Glue/EMR, Lambda, Bedrock or equivalent, OpenSearch, KMS.
Has built a data trust or governance layer for data without native RBAC: classification, masking, lineage, policy enforcement.
Proven track record managing a distributed US–India team.
Strong written communication; comfortable presenting to executives and customers.

Nice To Haves

Experience supporting sales or revenue agents, or CRM-grounded AI applications.
Familiarity with dbt Semantic Layer, Cube, AtScale, or comparable metric platforms.
Prior experience at an integration platform, iPaaS, or data fabric company.
Open-source contributions in data infrastructure (dbt, Neo4j, a vector DB, etc.).

Responsibilities

Define and evolve the reference architecture across the knowledge graph, semantic layer, vector retrieval, and hosted trust layer that grounds every AI agent.
Hire and grow the data engineering team
Own the trust layer charter: classification, masking, lineage, and policy enforcement for data that lacks native role-based access control.
Partner with the Director of AI (peer) on the data-substrate-to-agent-development handoff — your team produces the substrate, their team consumes it to build Sales Agents and other enterprise AI applications.
Run quarterly planning with the VP of AI and other business teams.. Translate the agent roadmap into data investments.
Establish SLOs for retrieval quality, data freshness, and trust-layer policy compliance.
Lead async, multi-geo collaboration norms; defend team scope against feature-factory pressure.