Data & Integration Engineer - Chicago, IL

Company Description

Work with Us. Change the World.

At AECOM, we're delivering a better world. Whether improving your commute, keeping the lights on, providing access to clean water, or transforming skylines, our work helps people and communities thrive. We are the world's trusted infrastructure consulting firm, partnering with clients to solve the world’s most complex challenges and build legacies for future generations.

There has never been a better time to be at AECOM. With accelerating infrastructure investment worldwide, our services are in great demand. We invite you to bring your bold ideas and big dreams and become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and other professionals delivering projects that create a positive and tangible impact around the world.

We're one global team driven by our common purpose to deliver a better world. Join us.

Job Description

AECOM Hunt is growing its Digital Engineering team and is seeking a Data & Integration Engineer to design and build production-grade integrations and automation systems that move data from operational sources into a central, structured repository, and then enable internal applications and AI/LLM-powered workflows that support construction operations and project delivery. 

You will help ensure data is usable beyond reporting—supporting agent-driven retrieval, summarization, and decision support. You will work closely with construction professionals, analysts, and technologists to deliver reliable data systems, plus the tools and APIs that make that data usable by the business. 

This role sits within a construction delivery organization. Success in this role requires comfort working with real-world, project-based operational data that is often messy, fragmented, and shaped by schedules, budgets, contracts, field workflows, and legacy systems. Candidates who have experience with construction, engineering, manufacturing, or other asset- and project-heavy industries will be particularly well-suited for this position. 

This role sits at the intersection of data engineering, systems integration, automation, and business-facing application development, with increasing emphasis on AI-enabled workflows. This is not a dashboard-only or “pipeline babysitting” role. You will be expected to ship end-to-end integrations into production, including authentication, incremental loads, failure handling, monitoring/alerting, and documentation. 

You should be comfortable working across messy source systems, defining data contracts, and delivering production-ready automations. 

(Construction Systems + AI Automation) 
Location: On-site (5 days/week), Chicago, IL (No Remote Option) 

Key Responsibilities 

• Design and maintain production-scale integrations (API, database, file-based, event-driven) that reliably deliver data into a central repository. 
• Build and support a centralized data platform that powers analytics, internal applications, and AI/LLM-enabled workflows. 
• Own integrations end-to-end: source ingestion → normalization → storage → access patterns (BI, APIs, apps, agents). 
• Implement data normalization and metadata standards so datasets can be reliably reused across teams and products. 
• Translate business needs into scalable technical solutions, including automation and AI-assisted workflows where appropriate. 
• Improve reliability through monitoring, alerting, observability, data validation, and failure recovery (retries, idempotency, backfills). 
• Contribute to architecture and engineering standards for integration patterns, data modeling, and AI-ready data access (clean interfaces, traceability, auditability). 
• Build and support internal tools and services (web apps, APIs, utilities) that allow business teams to discover, search, and operationalize data. 
• Enable AI/LLM use cases (semantic search, summarization, classification, structured extraction, agent workflows) by implementing repeatable pipelines, structured outputs, evaluation/QA, and human-in-the-loop controls. 
• Design and maintain data access patterns (views, APIs, query endpoints) that support low-latency application usage and governed analytics. 
• Evaluate and recommend tools/platforms pragmatically; the priority is outcomes and maintainability, not specific products. 
• Debug issues across integrations, data storage, and applications; write clear documentation including data lineage, contracts, and runbooks. 

Platform & Architecture Focus 

• Tool-agnostic environment; we prioritize strong engineering fundamentals over specific vendors. 
• Typical sources include construction and enterprise platforms such as ERP (e.g., CMiC, Textura), project m