Head of Data Engineering

Agilent Technologies•Santa Clara, CA

19h•$171,600 - $321,750•Onsite

About The Position

Owns the Fabric data plane: the certified data and semantics every agent and BI use case depends on. The Agilent Intelligence Fabric is a single governed substrate serving both BI and agentic AI; one substrate, two consumption modes. This role makes the data side of that promise real, meaning every certified data product carries a semantic definition, a data contract, policy and entitlement metadata including agent identity, lineage and observability, and a certification tier. The role operates on a core conviction of the program: AI is the primary builder of the Fabric, not merely its consumer. This leader deploys agents that generate metadata, resolve entities across domains, score quality, and classify unstructured content, so the data plane compounds in richness with every interaction rather than depending on manual annotation at enterprise scale. Leads a team responsible for designing, developing, and implementing modular data models, data pipelines, and data management frameworks that enable the capture, integration, storage, and utilization of structured and unstructured data from multiple sources. Applies in-depth understanding of business and technical requirements to define data engineering priorities, direct the development of scalable data solutions, and establish standards and processes that ensure data reliability, efficiency, quality, compatibility, and accessibility. Provides technical and organizational leadership in the development of data platforms and tools that support analytics, data science, predictive and prescriptive modeling, and automation initiatives, while overseeing project execution, cross-functional collaboration, talent development, and continuous improvement of data engineering capabilities to meet evolving business and product requirements.

Requirements

A track record building enterprise data platforms that serve production AI systems, not only analytics; experience with semantic layers, ontologies, or knowledge representation at scale.
Deep familiarity with the modern lakehouse, vector, and graph landscape; experience with Microsoft Fabric, Snowflake, or equivalent platforms in a multi-cloud estate.
Experience operating data contracts, lineage, and certification models in a regulated or quality-driven industry; life sciences or GxP exposure is a strong plus.
Curiosity about AI, its potential and its pitfalls. The field moves monthly, and the people who thrive here are genuinely curious about both sides of it: what these systems can newly do, and where they fail, mislead, or quietly degrade. We want people who read the failure analyses as eagerly as the launch posts, who experiment on their own initiative, and who hold excitement and skepticism at the same time without letting either one win permanently.
Lifelong learners. Whatever expertise a candidate arrives with will be partially obsolete within a year, and that is not a defect of the candidate; it is the condition of the field. We hire people who have reinvented their toolkit before and expect to do it again, who learn in public, and who treat being wrong as information rather than injury. A history of deliberate self-reinvention counts for more than any single credential.
Excellent communication and the ability to influence. Nothing in this organization ships by authority alone. Every role here persuades: domain experts to engage, stewards to share what they know, sponsors to stay honest about value, and functions like Legal, Quality, and Security to move from gatekeeping to partnership. We look for people who write and speak clearly, who adapt their register from bench scientist to Board, and who change minds through credibility and clarity rather than escalation.
The instinct to automate curation with AI rather than scale it with headcount.
Bachelor’s or Master’s Degree or equivalent.
Broad knowledge of functional area(s) of responsibility.
Minimum of 10 years' experience formally or informally leading people, projects and/or programs.

Responsibilities

Certified, versioned data products and semantic models, built in partnership with domain owners and stewards, with certification tiers that agents and BI consumers can both trust.
The Asset Registry, lineage, and data-quality signals; the registry is the discoverable, versioned home for data products and semantic definitions.
Lakehouse, vector, and graph retrieval foundations underpinning grounded agent behavior.
Agentic workloads that build the Fabric itself: metadata generation, entity resolution, quality scoring, and unstructured content classification.
Solid-line management of AI Data Engineers deployed into pods.