Data & Integration Engineer - Chicago, IL

About the Role

You will own end‑to‑end integrations—including authentication, incremental loads, failure handling, monitoring, alerting, and documentation—define data contracts, normalize data, and deliver APIs and tools that make data usable for analytics, internal applications, and AI agents. Success requires delivering reliable data systems and the tools that allow business teams to discover, search, and operationalize that data. Success in this role is measured by building multiple production integrations, delivering a data‑to‑application solution adopted by the business, implementing robust monitoring and documentation, and establishing repeatable AI automation patterns. • Design and maintain production‑scale integrations (API, database, file‑based, event‑driven) delivering data to a central repository • Build and support a centralized data platform powering analytics, internal applications, and AI/LLM workflows • Implement data normalization and metadata standards for reliable reuse across teams • Translate business needs into scalable technical solutions, including automation and AI‑assisted workflows • Improve reliability with monitoring, alerting, observability, data validation, and failure recovery • Contribute to architecture and engineering standards for integration patterns, data modeling, and AI‑ready access • Develop internal tools and services (web apps, APIs, utilities) enabling data discovery and operationalization • Enable AI/LLM use cases (semantic search, summarization, classification, extraction, agent workflows) with repeatable pipelines and human‑in‑the‑loop controls • Design data access patterns (views, APIs, query endpoints) supporting low‑latency usage and governed analytics • Evaluate and recommend tools/platforms pragmatically, prioritizing outcomes and maintainability • Debug issues across integrations, storage, and applications; produce clear documentation with lineage, contracts, and runbooks • Build systems with monitoring, alerting, and reliable failure recovery mechanisms • Integrate LLM/AI into automation workflows with structured outputs, evaluation, and handling of hallucinations or low confidence • Apply software engineering best practices: version control, code review, testing, and secret management • Design data platforms supporting reporting, APIs, applications, and AI agents • Deploy internal services/tools used by non‑technical teams • Communicate effectively with non‑technical stakeholders • Build three or more production integrations into the central repository • Deliver at least one data‑to‑application outcome adopted by the business • Implement monitoring, alerting, and comprehensive documentation for supportability • Establish repeatable patterns for structured extraction and AI automation with QA/evaluation

Key Responsibilities

• ▸integration development
• ▸data platform
• ▸system monitoring
• ▸ai automation
• ▸tool development
• ▸stakeholder communication

What You Bring

The position sits at the intersection of data engineering, systems integration, automation, and business‑facing application development, with an increasing focus on AI‑enabled workflows. Candidates should be comfortable handling messy, project‑based operational data typical of construction, engineering, or manufacturing environments. Platform & Architecture: A tool‑agnostic environment consolidates data from sources such as ERP (CMiC, Textura), project management (ACC, Procore), document management, and scheduling tools (Primavera P6) into a centralized, queryable repository supporting structured tables, document metadata, and vector/semantic indexing for AI retrieval workflows. Integrations must be production‑grade, featuring secure authentication, observability, and recoverability. Qualifications: A bachelor's degree in Computer Science or a related field and at least four years of experience in data engineering, systems integration, backend engineering, or automation are required, with strong SQL, Python, and API/ETL skills. Experience in construction or similar asset‑heavy industries, as well as familiarity with monitoring, AI/LLM integration, and software engineering best practices, is highly preferred. • Own integrations end‑to‑end: ingestion, normalization, storage, and access via BI, APIs, apps, or agents • BA/BS in Computer Science or related field with 4+ years in data engineering, integration, or automation • Strong SQL expertise and ability to design operational and analytical schemas • Proficiency in Python or similar language for APIs, transformation, and automation tooling • Experience with APIs and ETL/ELT workflows, handling pagination, rate limits, retries, incremental loads, and secure credentials • Familiarity with orchestration/integration frameworks (scheduling, triggers, queues/events, dependency management) • Understand data modeling, governance, privacy, permissions, and system reliability • Prior experience in construction, engineering, manufacturing, or similar project‑heavy industries

Requirements

• ▸sql
• ▸python
• ▸apis
• ▸etl
• ▸data engineering
• ▸construction

Benefits

The position is onsite in Chicago, IL, does not offer remote work, relocation, or sponsorship. AECOM provides comprehensive benefits, including health, retirement, stock purchase, and professional development programs, and emphasizes a collaborative, growth‑focused workplace.

Work Environment

Office Full-Time