Senior Manager, Data Engineering
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Job Description
Senior Manager in Data Engineering leading data platform strategies at JLL. Driving data governance and technical leadership across engineering teams in a global real estate firm.
Responsibilities:
- 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.
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
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