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Description

Lead the enterprise Data Engineering & AI delivery organization, building and operating shared, reusable capabilities that turn prioritized business demand into secure, scalable, production-grade data and AI products. Reporting to the Managing Director, Data & AI, this leader is one of three Data & AI leadership pillars and partners with IT on enterprise platforms while owning Data & AI engineering execution, talent, standards (including the engineering playbook and training approach), the enterprise ontology and AI brain, the technical controls for AI governance, and delivery performance. Because Burns & McDonnell runs multiple data and AI platforms (including Azure, AWS, GCP, Palantir, and Databricks), this hands-on technical leader brings working depth on each of them to drive consolidation into one delivery system. The Director delivers against the Shared Business Backlog and portfolio priorities set through the Operating Model & Strategy function, and commits technical capacity to those priorities.

Key Responsibilities

Organization & Talent

·Lead and develop the Data & AI technical organization, including data engineering, integration, analytics engineering, applied AI/ML, technical pod leadership, and product support/run capabilities in the approved structure.

·Attract, hire, and retain top Data & AI engineering talent, and build enduring capability through career paths, succession depth, mentoring, and knowledge sharing so the function does not depend on a few individuals.

·Lead and unify teams across multiple geographies and time zones, with consistent standards, ways of working, and collaboration.

·Drive build-versus-buy decisions, vendor/SI performance, technical workforce planning, and skills development.

Delivery

·Own the technical delivery roadmap and turn prioritized backlog demand into capacity plans, pod assignments, and production releases. Stay hands-on enough to set the technical bar and unblock teams.

·Run all approved platforms (Azure, AWS, GCP, Palantir, Databricks, and others) as one delivery system working from one backlog, with common standards, reusable components, and shared ways of working rather than separate platform teams. Lead consolidation where platforms overlap.

·Make sure every product has a named owner and a funded support/run model before launch, so nothing is left unsupported after go-live.

·Deliver AI to production through agents and agentic workflows, with evaluation, observability, guardrails, and cost tracking for AI usage built in from the start.

·Set and report engineering quality, reliability, delivery, and capacity metrics. Raise technical risks and constraints early, with a recommended path.

Standards & Engineering

·Define engineering standards and reusable patterns for ingestion, integration, transformation, modeling, data products, analytics, AI/ML, testing, CI/CD, observability, and production support.

·Partner with IT, Enterprise Architecture, Security/Privacy, and platform teams on architecture decisions, cloud/platform services, access controls, resilience, and enterprise guardrails.

·Own the Data & AI Engineering Playbook and the training approach across the organization, including onboarding, skills paths, and certification, so teams, pods, and partners/SIs build to the same practices.

·Build and operate the Burns & McDonnell ontology and enterprise AI brain: the shared knowledge, semantic, and context layer that grounds AI in business definitions, relationships, and rules. Build it at the enterprise level and support local ontologies and AI context for portfolios and business units, kept aligned through common standards and governed reuse.

·Implement the technical controls for AI governance (access controls, guardrails, evaluation, lineage, logging and audit, and human oversight). Work with the Director, Operating Model & Strategy, who orchestrates AI governance decisions and workflows across IT, Security, Legal, and Risk.

·Promote reuse across portfolios so capabilities are built once and leveraged enterprise-wide.

Stakeholder Influence

·Influence senior business, IT, Enterprise Architecture, and Security stakeholders to align on technical direction, guardrails, and platform decisions.

·Manage business and executive expectations transparently on capacity, feasibility, sequencing, and trade-offs, and reliably deliver on committed plans.

·Build credibility with sponsors and portfolio leaders through predictable, production-grade delivery and early, honest surfacing of risks and constraints.

·Champion reuse and enterprise standards across autonomous portfolios through persuasion and demonstrated value rather than mandate.

What Success Looks Like (First 12 Months)

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Roadmap and standards: Technical delivery roadmap and engineering standards approved and published, aligned to the enterprise Data & AI roadmap.

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One delivery system: All approved platforms running through one backlog and one delivery system, with a platform consolidation plan agreed with IT and Enterprise Architecture.

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Capacity: Pods staffed and delivering against committed capacity, with technical capacity visible in the integrated portfolio view.

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Talent: Key roles filled with top talent, retention and succession coverage in place for critical skills, and onboarding aligned to the engineering playbook.

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Metrics: Baseline quality, reliability, delivery, and capacity metrics reported monthly to the Managing Director.

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Sustainment: A named support/run owner and model in place for every product in production.

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Playbook and training: Data & AI Engineering Playbook published and a training approach launched across the organization, with adoption tracked.

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Ontology and AI brain: Enterprise ontology and AI brain foundation live, with agreed approach for local ontologies and a first set of business domains modeled and used by AI applications.

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AI governance: Technical AI governance controls (access, guardrails, evaluation, audit logging) operating on production AI, with exceptions visible to the governance forums.

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Reuse: Reuse targets set and tracked, with reusable patterns adopted across portfolios. Targets to be set with the Managing Director at the start of the role.

Qualifications

+ Bachelor's degree in computer science, engineering, or a related field from an accredited program and a minimum of 15 years of related professional experience in data engineering, data platforms, integration, analytics engineering, AI/ML engineering, or related hands-on technology disciplines, with a track record of building and shipping production data and AI solutions; substantial people-leadership experience.

+ Experience leading enterprise-scale data engineering/platform teams and multiple concurrent delivery streams in a complex organization.

+ Deep knowledge of modern cloud data architectures, ing...

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Department Manager, Data Engineering & AI

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Department Manager, Data Engineering & AI