Note: The job is a remote job and is open to candidates in USA. JLL is a leading professional services firm specializing in real estate and investment management. They are seeking a Senior Manager, Data Engineering to lead the Enterprise Platform Data Engineering team, focusing on data strategy, quality initiatives, and delivery of data platforms that support the organization’s goals.
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
Skills
- 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
- 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
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
Company Overview