Director, Data Engineering & AI

Aramark•Philadelphia, PA

2d•$150,000 - $160,000•Remote

About The Position

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.

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.
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.
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; advanced degree in a quantitative discipline preferred.
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.

Nice To Haves

Advanced degree in a quantitative discipline preferred.

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.