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 tooling to the team

AI & Data Engineering

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

Collaboration & Stakeholder Engagement

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

Qualifications

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

Preferred

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

This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.

Estimated compensation for this position:

160,000.00 – 200,000.00 USD per year

This range is an estimate and actual compensation may differ. Final compensation packages are determined by various considerations including but not limited to candidate qualifications, lo