Sr. Director, Data Engineering

JLL•Chicago, IL

2d•$229,000 - $309,000•Onsite

About The Position

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. 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. We are seeking an experienced Sr. Director, Data Engineering to lead the REMS (Real Estate Management Services) Data Engineering team within JLL Technologies. In this role, you will own people leadership, delivery, and technical direction for the data platforms and pipelines that power REMS products across JLL's global real estate management operations — spanning property and lease data, facilities workflows, work order management, and operational intelligence. You will set priorities in close partnership with REMS Product and Engineering leadership, govern the technical roadmap, grow a high-performing team, and ensure the delivery of reliable, well-governed data assets that scale with the business. The scope spans platform strategy, data architecture, team development, and stakeholder alignment across REMS and enterprise data functions. The ideal candidate blends Principal Data Engineer-level technical credibility — expert data engineering, cloud architecture, and cross-domain platform delivery — with proven experience managing through leads and managers, navigating complex enterprise environments, and translating real estate management data challenges into durable engineering capabilities.

Requirements

5+ years of people management experience, including at least 1–2 years managing managers or team leads — not just ICs — with accountability for performance, staffing, delivery outcomes, and team development across multiple scrum teams.
10+ years of data engineering experience across multiple large, complex projects and technology domains.
Expert proficiency in Python and SQL; strong distributed data processing experience with PySpark/Spark; demonstrated ability to architect complex end-to-end data solutions.
5+ years hands-on with cloud platforms (Azure or AWS), including advanced data services such as Databricks, Azure Data Factory, Synapse, AWS Glue, or Redshift.
Proven data modeling expertise across relational, dimensional, and NoSQL schemas (CosmosDB, MongoDB, PostgreSQL); experience designing data structures that balance performance, scalability, and analytical consumption.
Demonstrated experience establishing data governance frameworks in a production enterprise environment — data quality standards, lineage tracking, ownership models, and compliance controls.
Experience orchestrating multiple data engineering project teams simultaneously; track record of shaping organizational data strategy and aligning engineering priorities with business outcomes.
Exceptional communication and stakeholder management skills — able to earn trust quickly, drive alignment across product and engineering, and present technical trade-offs clearly to executive audiences.

Nice To Haves

Experience leading data engineering for a real estate, facilities, lease management, or asset-intensive operational domain.
Familiarity with REMS or related property management platforms (e.g., MRI, Yardi, IBM Maximo, ServiceNow FM).
Hands-on experience with agentic AI, RAG architectures, vector databases, or MCP integrations for building AI-ready data foundations.
Experience with data orchestration tools such as Airflow, Prefect, or dbt for managing complex pipeline dependencies and transformations.
Strong DevOps and DataOps practices — CI/CD pipeline design, containerization (Docker, Kubernetes), infrastructure-as-code (Terraform or CloudFormation), and observability tooling.
Experience with data governance compliance standards (GDPR, CCPA, data residency requirements) and enterprise security practices for sensitive data.
Master's degree in Computer Science, Engineering, Data Science, or a related field.

Responsibilities

Define and drive the REMS data platform strategy, consolidating fragmented data sources and legacy pipelines into a unified, governed, and scalable architecture that serves as the foundation for analytics, AI, and operational decision-making across real estate management.
Provide hands-on technical leadership across the REMS Data Engineering team and related initiatives — setting architectural standards, design patterns, and engineering best practices that raise the quality bar across the organization and ensure alignment with enterprise platform standards.
Oversee the design, delivery, and continuous improvement of REMS data pipelines, APIs, and backend data services that ingest, transform, and serve property, lease, facilities, and operational data to downstream products, analytics, and AI systems.
Own data modeling standards across REMS — including relational, dimensional, and NoSQL schemas — ensuring data structures are designed for performance, maintainability, and reliable consumption by business intelligence, data science, and application teams.
Establish DataOps practices, data quality frameworks, lineage tracking, and compliance controls that ensure REMS data products are production-ready, auditable, and trusted — with clear ownership models and monitoring across all data assets.
Architect integration patterns and API strategies that enable seamless data access across REMS applications, analytics platforms, and enterprise systems — including event-driven patterns and consumption standards for both internal and external data consumers.
Partner with REMS Product, Application Engineering, Enterprise Data, and business stakeholders to align data platform capabilities with product strategy and operational priorities — translating complex data challenges into actionable roadmaps with measurable outcomes.
Hire, develop, and retain data engineers and team leads across all levels; conduct architecture and delivery reviews, provide technical guidance, and build a team culture defined by ownership, curiosity, and continuous improvement.
Serve as the data engineering voice in product reviews, architecture forums, and executive presentations — communicating roadmap, trade-offs, and technical direction with clarity and confidence to both technical and business audiences.