Senior Manager, Data Engineering

Role Overview

• Lead the Enterprise Platform data engineering function—defining the roadmap, operating model, and standards that implement JLL’s company data strategy and data quality programs at enterprise scale.
• Align team execution with JLL’s enterprise data strategy while delivering the platform capabilities that all business units depend on.
• 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.
• Provide technical leadership across the Enterprise Platform Data Engineering team and related initiatives, setting architectural standards, design patterns, and best practices.
• Architect sophisticated integration strategies for structured and unstructured data sources.
• Design and implement enterprise-grade data integration frameworks.

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.

Tech Stack

• Amazon Redshift
• AWS
• Azure
• Cassandra
• Cloud
• Java
• MongoDB
• Neo4j
• NoSQL
• Postgres
• PySpark
• Python
• Scala
• Spark
• SQL

Benefits

• 401(k) plan with matching company contributions
• Comprehensive Medical, Dental & Vision Care
• Paid parental leave at 100% of salary
• Paid Time Off and Company Holidays
• Early access to earned wages through Daily Pay