Staff Data Engineer

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.

Key Responsibilities

Solution Design & Architecture

•       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

Rapid Prototyping & Solution Delivery

•       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

AI Agent Development & Agentic Systems

•       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 tooli