Senior Manager, Data Engineering

JLL•Chicago, CO

2d•Remote

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. About JLL and JLL Technologies 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.

Requirements

3+ years of experience directly managing data engineers or equivalent software/data teams, including performance management, staffing, and delivery accountability.
10+ years of experience in data engineering and Big Data development, with extensive experience architecting and delivering enterprise-scale, fault-tolerant data platforms.
5+ years of hands-on experience with cloud platforms such as Azure or AWS, including advanced services (e.g., Databricks, Azure Data Factory, Synapse, AWS Glue, EMR, Redshift).
Expert-level proficiency in multiple server-side programming languages including Python, Java, and Scala, with deep expertise in PySpark/Spark for distributed data processing at scale.
Proven expertise in data modeling, data architecture, and designing data systems that balance performance, scalability, maintainability, and cost.
Deep understanding of machine learning lifecycle, MLOps practices, model governance, and production ML systems.
Extensive experience working with diverse data technologies including SQL databases (e.g., Azure SQL, PostgreSQL), NoSQL databases (e.g., Cosmos DB, MongoDB, Cassandra), and AI-centric databases such as vector databases (e.g., Pinecone, Weaviate) and knowledge/graph databases (e.g., Neo4j, Amazon Neptune).
Demonstrated ability to architect and optimize complex data systems for performance, reliability, and cost-efficiency.
Proven track record of technical leadership, including mentoring senior engineers and leading cross-functional initiatives.

Nice To Haves

Master’s degree in computer science, Engineering, Data Science, or a related field.
Deep expertise in designing and implementing semantic layers, ontologies, and knowledge graphs for enterprise data systems.
Extensive experience with streaming architecture using Kafka, Spark Streaming, Flink, or similar technologies.
Expert-level understanding of DevOps principles, with hands-on experience designing CI/CD pipelines, infrastructure as code (Terraform, CloudFormation), and container orchestration (Kubernetes, EKS, AKS).
Significant experience with LLM-driven workflows, advanced prompt engineering, RAG (Retrieval-Augmented Generation) architectures, and orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI, AutoGen).
Deep familiarity with AI-powered development tools and practices, driving adoption of AI-augmented software development lifecycles across teams.
Experience with data governance frameworks, compliance standards (GDPR, CCPA), and enterprise security practices.
Published technical articles, conference presentations, or contributions to open-source projects in data engineering or related fields.

Responsibilities

Define and drive the Enterprise Platform data strategy—implementing JLL’s company data strategy and consolidating siloed data sources into a unified, governed, and scalable data architecture that serves as the foundation for analytics, reporting, and decision-making across the business.
Provide technical leadership across the Enterprise Platform Data Engineering team and related initiatives, setting architectural standards, design patterns, and best practices that elevate data engineering quality organization.
Architect sophisticated integration strategies for structured and unstructured data sources, enabling advanced analytics, AI/ML model inputs, and real-time insight generation at enterprise scale—keeping data at the center, not the application layer.
Lead the design and delivery of robust platform capabilities that bridge diverse data domains, enabling intelligent search, contextual recommendations, and automated insight generation through well-governed data services and APIs.
Design and implement enterprise-grade data integration frameworks—including API strategies, event-driven patterns, and consumption standards—enabling seamless data access across analytics platforms, operational applications, and Agentic AI systems.
Partner with data science and ML teams to architect production-ready data foundations, establishing MLOps-compatible data pipelines, feature stores, and model input governance at scale.
Guide the design of semantic layers, ontologies, and knowledge graph-style approaches that make enterprise data intelligently discoverable and reliably consumable by both human stakeholders and AI/ML systems.
Lead data architecture reviews, technology evaluations, and proof-of-concept initiatives. Drive adoption of emerging data engineering best practices and modern stack components across the team.
Establish enterprise-grade DataOps practices, observability frameworks, data quality standards, lineage tracking, and compliance controls ensuring data products are production-ready, auditable, and trusted by stakeholders.
Partner with executive leadership, Product, and Business teams to align data platform capabilities with JLL’s enterprise data strategy. Translate complex business challenges into prioritized data roadmaps with measurable outcomes.
Hire, mentor, and grow data engineers across all levels; conduct technical reviews, provide architectural guidance, and build a culture of technical excellence, ownership, and continuous improvement.
Serve as the data engineering voice in executive and cross-functional forum communicating roadmap, trade-offs, and strategic direction to diverse audiences with clarity and confidence.