This role pays more than
83%
of similar roles. Most pay
$162,892–$254,750
— the shaded band above.
At the midpoint, this role pays about
$269k
versus about
$209k
for comparable roles.
Based on 240 similar postings.
Employer
About JLL (Jones Lang LaSalle)
JLL (Jones Lang LaSalle) is a global professional services firm specializing in real estate and investment management, providing services to buyers, sellers, tenants, landlords, investors, and developers. Industry: Commercial Real Estate Services
JLL (Jones Lang LaSalle) currently has
75 open roles
on FindRole.
Listed pay typically runs
$147,000–$190,000
across 35 roles with salary data.
The Sr. Director, Data Engineering leads the REMS Data Engineering team within JLL Technologies to oversee data platforms and pipelines powering real estate management operations. This role involves managing people, defining technical roadmaps, and establishing engineering standards for property, lease, and facilities data. The successful candidate will build scalable architecture, manage data modeling across relational and NoSQL schemas, and implement DataOps practices to ensure high-quality, governed assets. Key technologies include Python, SQL, PySpark, and cloud platforms such as Azure or AWS, specifically utilizing Databricks, Azure Data Factory, Synapse, AWS Glue, or Redshift. The role addresses complex real estate management challenges by transforming fragmented data into reliable capabilities for analytics and automated workflows. Essential skills include expertise in distributed processing, API strategy, and cross-functional leadership to align technical infrastructure with core business objectives.
Define and drive the REMS data platform strategy to consolidate fragmented sources into a unified, scalable architecture.
Establish technical standards, design patterns, and engineering best practices for the data engineering team.
Oversee the design and delivery of data pipelines, APIs, and backend services for property and lease data.
Manage data modeling across relational, dimensional, and NoSQL schemas to ensure performance and maintainability.
Implement DataOps practices, including quality frameworks, lineage tracking, and compliance controls for production-ready assets.
Architect integration patterns and API strategies to enable seamless data access across internal and external systems.
Lead the hiring, development, and mentorship of data engineers and team leads.
Serve as the primary technical voice in product reviews and executive presentations to align roadmap with business goals.
What we're looking for
Must have 5+ years of experience in people management, including at least 1–2 years managing managers or team leads.
Must have 10+ years of data engineering experience across multiple large, complex projects and technology domains.
Expert proficiency in Python and SQL with strong distributed data processing experience using PySpark/Spark.
Must have 5+ years of hands-on experience with cloud platforms (Azure or AWS) and advanced services like Databricks, Azure Data Factory, Synapse, AWS Glue, or Redshift.
Must possess expertise in data modeling across relational, dimensional, and NoSQL schemas including CosmosDB, MongoDB, and PostgreSQL.
Must have proven experience establishing data governance frameworks, including quality standards, lineage tracking, and compliance controls in a production enterprise environment.
Must demonstrate exceptional communication skills to align engineering priorities with business outcomes and present technical trade-offs to executive audiences.
Master's degree in Computer Science, Engineering, Data Science, or a related field.
Python
SQL
Spark
AWS
Microsoft Azure
CI/CD
DataOps
DevOps
AI/ML
Generative AI
Agentic AI
Data Governance
Data Modeling
Data Engineering
Cybersecurity
NetFlow
SIEM
Python
SQL
Scala
Java
Azure
AWS
GCP
Microsoft Fabric
Azure Databricks
Snowflake
Big Query
Azure Data Factory
Air Flow
Cosmos DB
Azure Functions
GitHub
Git
CI/CD
Azure DevOps
Data Modeling
Python
Scala
Java
SQL
Azure
AWS
GCP
Azure Synapse
Azure Databricks
Redshift
Big Query
Snowflake
Azure Data Factory
Cosmos DB
Event Hubs
Azure Storage
Azure Functions
Container Apps
CI/CD
Azure DevOps
GitHub
Git
ETL
ELT
Data Modeling
Data Warehousing
Data Lake
Test Driven Development
Data Engineering
AWS
Azure
GCP
AI
ML
MLOps
Data Lakes
Metadata Management
Distributed Data Systems
Data Quality
Enterprise Architecture
Software Engineering
Digital Platforms