Staff Data Engineer

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

• 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

Required

• 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

• 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