Senior Manager, Data Engineering

JLL•Chicago, IL

1d•$162,700 - $199,300•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. JLL Technologies is a specialized group within JLL. At JLL Technologies, our mission is to bring technological innovation to commercial real estate. We deliver unparalleled digital advisory, implementation, and services solutions to organizations globally. Our goal is to leverage technology to increase the value and liquidity of the world’s buildings, while enhancing the productivity and happiness of those that occupy them. The Data Engineering team builds and operates the data products and pipelines that power JLL’s Corporate Functions, including areas such as finance, HR, Legal, Compliance and shared services. Working on the Enterprise Data Platform (EDP), we turn internal enterprise data into trusted, governed, and increasingly real-time assets that leaders and teams across the firm rely on for reporting, planning, and operational decisions.

Requirements

2+ years directly managing data engineers or technical individual contributors; experience setting priorities, allocating work, and coaching teams; managing managers is not required.
7+ years in data engineering, including multiple large and complex projects delivered end to end.
Advanced Python and SQL; strong experience with Azure data services and distributed processing (e.g., Spark/PySpark); experience building reusable frameworks.
Hands-on experience designing or leading real-time data pipelines with Kafka, Spark Streaming, or equivalent technologies.
Working knowledge of data quality frameworks, lineage, metadata management, and data governance practices.
Experience managing or training teams of data engineers and setting priorities across multiple data engineering workstreams in an agile environment.
Strong written and verbal communication skills, with the ability to engage both technical and business stakeholders.

Nice To Haves

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Experience with corporate data domains such as finance, HR, or shared services (e.g., ERP, HRIS, or enterprise reporting data), or in commercial real estate.
Experience with NoSQL, graph, or multi-model databases (e.g., CosmosDB, MongoDB) and complex cross-system integrations.
Familiarity with data cataloging and governance tooling, and with CI/CD and infrastructure-as-code practices.
Experience with modern lakehouse or data platform architectures and enterprise data platforms.
Exposure to applying AI/ML or LLM-based capabilities to data engineering and data quality workflows.

Responsibilities

Provide hands-on technical direction for batch and streaming data pipelines built on Azure, Python, and Spark. Guide architecture and design reviews, set engineering standards for reusable frameworks, and stay close to critical technical risks.
Lead the design and operation of event-driven pipelines using technologies such as Kafka and Spark Streaming, so that financial, workforce, and operational data reaches downstream consumers with the latency and reliability the business requires.
Own data quality, lineage, cataloging, and stewardship practices for Corporate Functions data domains, including sensitive finance and HR data. Define measurable quality standards, automate validation and monitoring in pipelines, and partner with enterprise governance and security teams to ensure compliance with JLL data policies and access controls.
Set priorities and manage execution across multiple scrum teams. Balance roadmap commitments, technical debt, and operational demands; maintain predictable delivery; and adapt plans as business needs evolve.
Align team deliverables with the Enterprise Data Platform strategy. Drive adoption of platform standards, ingestion patterns, and cloud-native services, and contribute to modernization of legacy data assets.
Establish CI/CD, observability, and incident management practices that keep pipelines healthy. Ensure teams meet service-level expectations and learn from production issues through blameless reviews.
Collaborate with product, analytics, data science, platform, security, and governance teams to deliver end-to-end data solutions. Resolve dependencies, negotiate shared priorities, and keep teams aligned on outcomes.
Recruit, coach, and retain strong engineers and team leads. Set clear expectations, provide regular feedback, build career growth paths, and foster a culture of inclusion, ownership, and continuous learning.
Build trusted relationships with Corporate Functions business and technology leaders. Communicate status, risks, and trade-offs clearly, manage expectations on scope and timelines, and ensure engineering work maps to business value.
Partner with leadership on headcount, vendor, and cloud cost planning for your teams, optimizing talent utilization and cost efficiency across products and initiatives.