Staff Data Engineer

JLL•Westmont, IL

5d•$160,000 - $200,000•Onsite

About The Position

JLL empowers you to shape a brighter way. Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. About JLL and Data Engineering JLL is a leading professional services firm that specializes in real estate and investment management. Our vision is to reimagine the world of real estate, creating rewarding opportunities and amazing spaces where people can achieve their ambitions. In doing so, we will build a better tomorrow for our clients, our people, and our communities. The Data Engineering team at JLL sits at the intersection of technology and business impact, building the data foundations and intelligent solutions that power decisions across the enterprise. We move fast, think big, and deliver production-grade platforms that transform how JLL operates — combining deep technical expertise with a relentless focus on solving real problems for real users. Role Overview As a Forward Deployed Data Engineer, you will operate at the front lines of innovation — embedded directly with product, program, and engineering teams to rapidly design, architect, prototype, and deploy data-driven solutions that solve high-priority business problems. This is not a traditional engineering role with long delivery cycles. You will be expected to go from concept to working prototype in days, not months, while maintaining enterprise-grade standards for quality, security, and scalability. You bring an exceptional combination of skills: solution design and architecture experience, deep hands-on engineering across data and AI agents, and the interpersonal skills to earn trust, drive alignment, and influence without authority. You are equally comfortable whiteboarding a system architecture with senior stakeholders and writing production code the same afternoon. You have genuine curiosity about the “why” and “what” behind every problem — not just the “how” — and you thrive in fast, ambiguous environments where creative thinking and execution speed both matter.

Requirements

6+ years of professional software or data engineering experience, including solution design and architecture ownership
Demonstrated ability to architect end-to-end data and AI systems — from requirements through deployment — with clear documentation and stakeholder communication
Hands-on experience building AI agents, including defining agent skills, tool use, memory, and multi-step reasoning
Working knowledge of MCP (Model Context Protocol) — including building or consuming MCP servers to connect agents with external systems
Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel
Strong hands-on experience with data engineering: pipelines, ETL/ELT, data modeling, SQL and NoSQL databases
Proficiency in Python
Experience with cloud platforms (AWS, Azure, or GCP) and modern data stack tooling
Exceptional communication and interpersonal skills — you can earn trust quickly, navigate ambiguity, and drive alignment across diverse teams
Comfort working in fast-paced environments with shifting priorities and high ownership expectations

Nice To Haves

Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), semantic search, and building indexing systems and data processing pipelines
Familiarity with prompt engineering, fine-tuning, and LLM evaluation techniques
Experience with agent observability and tracing tools (LangSmith, Arize, Weights & Biases, or similar)
Experience with AWS AgentCore — building, deploying, and operating agents on the platform
Knowledge of data orchestration tools (Airflow, Prefect, or dbt)
Experience with containerization and CI/CD practices (Docker, Kubernetes, GitHub Actions)
Background in real estate, financial services, or other data-intensive enterprise domains
Experience facilitating technical discovery workshops, design sprints, or architecture reviews
Contributions to open-source agentic or data projects, or a portfolio demonstrating rapid, creative problem-solving

Responsibilities

Lead solution design for complex, cross-functional data and AI problems — from initial discovery through to technical blueprint
Define and communicate architecture decisions, trade-offs, and delivery approaches to both technical and non-technical audiences
Design scalable, modular systems that balance the need for speed with enterprise standards for reliability, security, and maintainability
Participate in architecture reviews and Critical Design Reviews (CDRs), ensuring alignment with enterprise patterns and platform standards
Create clear technical documentation: architecture diagrams, data flow maps, API contracts, and solution briefs
Design and deliver working prototypes for complex data and AI problems within compressed timeframes, often days to weeks
Bring genuine curiosity to every engagement — deeply understanding the business case, problem context, and constraints before converging on a solution
Balance speed of delivery with enterprise standards — your prototypes are production-ready, not throwaway
Continuously iterate on solutions based on direct feedback from product managers, program leads, and end users
Design, build, and deploy AI agents and multi-agent systems that automate complex workflows end-to-end
Develop and maintain agent skills — discrete, reusable capabilities that compose into larger agentic pipelines
Implement and extend MCP (Model Context Protocol) servers and clients to connect AI agents with enterprise tools, APIs, and data sources
Build agent orchestration layers using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel
Design evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability and safety in production
Stay current with the rapidly evolving agentic AI landscape and proactively introduce new techniques and tooling to the team
Build and deploy AI-powered features and pipelines that automate workflows, surface insights, and enhance decision-making
Design and implement scalable data pipelines, APIs, and backend services that serve both internal tools and customer-facing products
Integrate LLMs, RAG systems, and ML models into production data workflows
Own data modeling, transformation, and quality across the solutions you deliver
Embed directly with product, program, and engineering teams to co-define problems and co-deliver solutions
Influence technical direction and build alignment across teams without relying on formal authority
Communicate complex technical concepts clearly to non-technical business stakeholders — in writing, in meetings, and in executive presentations
Mentor and elevate junior engineers, sharing patterns and practices for agentic development, prompt design, and rapid delivery
Foster a collaborative, low-ego team culture where speed and quality go hand in hand