Director, Data Engineering & AI

Job Description

The Director, Data Engineering & AI Enablement is the senior-most technical leader and active builder for the data engineering and artificial intelligence capability supporting the Facilities Management line of business. This is explicitly not a role defined by supervision alone ? the person in this seat is expected to personally design, build, and ship production systems (pipelines, models, and AI applications) while also setting the technical strategy and leading the team that scales that work.

The Director operates at two altitudes at once: deciding where the AI/ML roadmap and data architecture should go, and getting directly into the work ? writing code, prototyping, and solving the hardest, highest-ambiguity problems personally ? especially in the early stages of a new initiative, before it's handed off to the broader team. The data surface this role owns spans the full breadth of the business ?

financial data, labor and workforce data, IoT and smart building sensor data, employee work and activity data, and client/operational data ? not any single domain. The role also carries overarching technical oversight of enterprise data architecture standards and governance, ensuring all engineering and AI solutions align with enterprise data principles. The Director manages a cross-functional team of data engineers, ML engineers, data scientists, data architects, and BI developers.

Compensation Data

COMPENSATION: The hourly rate or salary range for this position is $150,000 to $160,000. If both numbers are the same, that is the amount that Aramark expects to offer. This is Aramark?s good faith and reasonable estimate of the compensation for this position as of the time of posting. ?

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BENEFITS: Aramark offers comprehensive benefit programs and services for eligible employees including medical, dental, vision, and work/life resources. Additional benefits may include retirement savings plans like 401(k) and paid days off such as parental leave and disability coverage. Benefits vary by location and are subject to any legal requirements or limitations, employee eligibility status, and where the employee lives and/or works.

For more information about Aramark benefits,  . ?

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There is no predetermined application window for this position, the position will close once a qualified candidate is selected. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including, but not limited to, the Los Angeles County Fair Chance Ordinance for Employers, the California Fair Chance Act, and the San Francisco Fair Chance Ordinance to the extent that those laws apply to the opportunity.?

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Job Responsibilities

Build

• Architect and build production-grade data pipelines (batch and streaming) for the highest-priority or highest-ambiguity initiatives ? spanning financial, labor/workforce, and smart building sensor, employee work, and client/operational data ? setting the technical pattern the team scales from.
• Write code, design model architectures, and personally develop and deploy machine learning models and generative AI applications (including LLM-based solutions), particularly for new or unproven use cases before delegating to the team.
• Stay hands-on in production: debug critical-path issues, review production-critical code and model logic, and remain a credible technical authority by virtue of doing the work, not just overseeing it.

• Define and execute the AI/ML roadmap for the line of business, identifying high-value use cases across the full data landscape ? predictive maintenance, demand forecasting, workforce and labor optimization, financial and margin analytics, smart building performance, and intelligent client reporting.
• Provide overarching oversight and governance of enterprise data architecture standards, design patterns, and data modeling principles ? ensuring all engineering solutions, vendor integrations, and AI workloads conform to the established framework.
• Review and approve solution designs across engineering, AI, and vendor deliverables for alignment with data architecture standards; serve as the senior technical authority for data design decisions.
• Evaluate emerging AI technologies and vendors; build-versus-buy analysis with a bias toward owned, scalable capabilities.

• Stand up and mature MLOps practices: CI/CD for data and models, automated testing, model monitoring, drift detection, and retraining pipelines.
• Engineer real-time and near-real-time data products supporting operational dashboards, alerting, and client-facing analytics across data domains ? personally, where the problem is novel enough to require it.
• Establish responsible AI practices, including model governance, bias evaluation, explainability standards, and compliance with emerging AI regulations.
• Optimize cloud data and AI infrastructure for performance, reliability, and cost, partnering with enterprise platform teams.