Senior Manager, Data Engineering
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Key skills for this role
About the Role
JLL Technologies is seeking a Senior Manager, Data Engineering to lead the Enterprise Platform Data Engineering team. You will drive people leadership, delivery excellence, and technical direction for data platforms and pipelines that power JLL's enterprise data strategy.
Key Skills for This Role
Responsibilities
- Lead and mentor a team of data engineers, providing technical guidance, performance management, and career development
- Define and execute the Enterprise Platform Data Engineering roadmap, aligning team priorities with JLL's enterprise data strategy
- Architect and oversee enterprise scale data platforms and pipelines that consolidate siloed data sources into unified, governed, and scalable foundations
- Partner with cross functional stakeholders to deliver trusted data capabilities that enable advanced analytics, semantic layers, and intelligent data discovery
- Establish and drive DataOps best practices, including CI/CD pipelines, data quality frameworks, observability, lineage tracking, and compliance controls
- Guide technical architecture reviews, technology evaluations, and proof of concept initiatives
- Serve as the data engineering voice in executive forums, communicating roadmaps, trade offs, and strategic direction
Requirements
- 3+ years of direct people management experience leading data engineers or equivalent technical teams
- 10+ years of experience in data engineering and Big Data development
- 5+ years of hands on experience with cloud platforms (Azure or AWS), including advanced services such as Databricks, Azure Data Factory, Synapse, AWS Glue, EMR, or Redshift
- Expert level proficiency in multiple server side programming languages (Python, Java, Scala) with deep expertise in PySpark/Spark for distributed data processing at scale
- Proven expertise in data modeling, data architecture, and designing scalable data systems
- Deep understanding of machine learning lifecycle, MLOps practices, model governance, and production ML systems
- Extensive experience with diverse data technologies including SQL databases, NoSQL databases, and AI centric databases such as vector databases and graph databases
Full Job Posting
What this job involves
- We are seeking an experienced Senior Manager, Data Engineering, to lead the Enterprise Platform Data Engineering team at JLL Technologies.
- In this role, you will drive people leadership, delivery excellence, and technical direction for data platforms and pipelines that power JLL's enterprise data strategy.
- This is an opportunity to shape how data engineering enables data governance, master data management, and enterprise wide data discoverability.
What your day to day will look like
- Lead and mentor a team of data engineers, providing technical guidance, performance management, and career development while fostering a culture of excellence and continuous improvement.
- Define and execute the Enterprise Platform Data Engineering roadmap, aligning team priorities with JLL's enterprise data strategy and translating business challenges into measurable technical outcomes.
- Architect and oversee enterprise scale data platforms and pipelines that consolidate siloed data sources into unified, governed, and scalable foundations for analytics, AI/ML, and real time decision making.
- Partner with cross functional stakeholders—including Product, Business, Data Science, and Executive Leadership—to deliver trusted data capabilities that enable advanced analytics, semantic layers, and intelligent data discovery.
- Establish and drive DataOps best practices, including CI/CD pipelines, data quality frameworks, observability, lineage tracking, and compliance controls to ensure production ready, auditable data products.
- Guide technical architecture reviews, technology evaluations, and proof of concept initiatives to drive adoption of modern data engineering practices and emerging technologies across the organization.
- Serve as the data engineering voice in executive forums, communicating roadmaps, trade offs, and strategic direction with clarity to diverse technical and non technical audiences.
Required Qualifications
- 3+ years of direct people management experience leading data engineers or equivalent technical teams, including staffing, performance management, and delivery accountability.
- 10+ years of experience in data engineering and Big Data development, with proven expertise architecting and delivering enterprise scale, fault tolerant data platforms.
- 5+ years of hands on experience with cloud platforms (Azure or AWS), including advanced services such as Databricks, Azure Data Factory, Synapse, AWS Glue, EMR, or Redshift.
- Expert level proficiency in multiple server side programming languages (Python, Java, Scala) with deep expertise in PySpark/Spark for distributed data processing at scale.
- Proven expertise in data modeling, data architecture, and designing scalable data systems that balance performance, maintainability, and cost.
- Deep understanding of machine learning lifecycle, MLOps practices, model governance, and production ML systems.
- Extensive experience with diverse data technologies including SQL databases (Azure SQL, PostgreSQL), NoSQL databases (Cosmos DB, MongoDB, Cassandra), and AI centric databases such as vector databases (Pinecone, Weaviate) and graph databases (Neo4j, Amazon Neptune).
Preferred Qualifications
- Master's degree in Computer Science, Engineering, Data Science, or a related field.
- Deep expertise designing and implementing semantic layers, ontologies, and knowledge graphs for enterprise data systems.
- Extensive experience with streaming architectures using Kafka, Spark Streaming, Flink, or similar technologies.
- Expert level understanding of DevOps principles with hands on experience in CI/CD pipelines, infrastructure as code (Terraform, CloudFormation), and container orchestration (Kubernetes, EKS, AKS).
- Significant experience with LLM driven workflows, RAG (Retrieval Augmented Generation) architectures, and orchestration frameworks (LangChain, LlamaIndex, CrewAI, AutoGen).
- 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.
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