Director, Data Engineering & AI

Aramark is a global leader in food and facilities services, and they are seeking a Director of Data Engineering & AI Enablement. The role requires a senior technical leader to design, build, and manage data engineering and AI capabilities, while also leading a cross-functional team to support the Facilities Management line of business.

Responsibilities:

• 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
• Recruit, lead, and develop a cross-functional team of data engineers, ML engineers, data scientists, and data architects — building a delivery-focused, AI-first engineering culture where the Director is seen as the strongest technical contributor in the room
• Partner with product and operations leaders to embed AI capabilities into frontline applications, IoT platforms, and client dashboards

Requirements:

• Expert-level, current proficiency in modern data engineering: Python, SQL, Spark, streaming frameworks (e.g., Kafka), orchestration tooling, and cloud data platforms (e.g., Snowflake, Azure) — able to write and review production code personally, not only at a conceptual level
• Proven, personal experience developing and deploying machine learning models and generative AI/LLM applications into production — this should reflect direct authorship, not solely direction of others' work
• Hands-on experience with MLOps tooling, CI/CD pipelines, containerization, and model monitoring frameworks
• Demonstrated track record of delivering AI products that generated measurable business outcomes
• Strong engineering leadership skills with experience building and scaling delivery-oriented technical teams — paired with a clear, recent record as a top individual technical contributor, not a manager who has drifted away from the technical work
• Comfortable moving fluidly between hands-on building (coding, prototyping, debugging production issues) and strategic work (roadmaps, architecture decisions, executive communication)
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field
• 7+ years of data and software engineering experience, including 4+ years building AI/ML solutions and 2+ years in engineering leadership roles
• Experience should reflect both strategic ownership (roadmap, architecture, team leadership) and sustained, recent hands-on technical contribution — candidates who have moved fully into management with no recent shipping experience are not a fit for this role
• Experience working across diverse, often messy enterprise data domains (financial, labor/workforce, IoT/sensor, employee activity, client/operational) preferred over deep specialization in any single domain
• Advanced degree in a quantitative discipline preferred