What this job involvesWe are seeking an experienced
Senior Manager, Data Engineering, to lead the
Enterprise Platform Data Engineering team. In this role, you will own people leadership, delivery, and technical direction for data platforms and pipelines that power JLL's enterprise data strategy, data quality initiatives, and foundational data capabilities across the organization. You will set priorities with Enterprise Platform and technology leadership, grow a high-performing engineering team, and ensure reliable, governed data assets that scale with the business. The ideal candidate blends hands-on data engineering credibility with proven experience managing engineers and partnering across product, business, and enterprise data functions.
What you'll get to doAs Senior Manager, your "customers" include
Enterprise Platform leadership and practitioners, enterprise data consumers, and partner engineering teams who depend on timely, trustworthy market and deal-related data. Your mission is to 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. You will align team execution with JLL's enterprise data strategy while delivering the platform capabilities that all business units depend on. This is an opportunity to shape how data engineering enables data governance, data quality, master data management, and enterprise-wide data discoverability-without owning full-stack product development as a core requirement for the role.
Key responsibilities- Strategic architecture and vision: 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.
- Technical leadership: 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.
- Advanced data integration: 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.
- Data platform innovation: 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.
- Enterprise integration architecture: 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.
- ML/AI platform strategy: 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.
- AI and knowledge architecture: 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.
- Technical excellence and innovation: 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.
- Enterprise DevOps and data governance: 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.
- Cross-functional leadership: 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.
- Team development and mentorship: 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.
- Stakeholder management: 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.
Who you areYou are a people leader who still speaks the language of data engineering-pipelines, platforms, and production operations. You care deeply about enterprise data platforms and enjoy connecting engineering work to company-wide data quality, governance, and strategic data outcomes. You balance strategic planning with execution discipline, and you create clarity for your team amid competing priorities. You build trust with stakeholders by delivering reliably and by developing engineers who grow in scope and impact.
Qualifications and skillsRequired qualifications- People management: 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.
Preferred qualifications- 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.
Key attributes- Leadership: Builds and retains strong teams; delegates effectively while staying close to critical technical risks.
- Stakeholder partnership: Navigates Enterprise Platform and cross-functional priorities with poise; negotiates scope and timelines with data-driven rationale.
- Judgment: Makes sound trade-offs among quality, speed, cost, and risk-especially where data is regulated or client-sensitive.
- Communication: Explains technical concepts clearly to executives and partners; listens to business constraints that should shape engineering decisions.
- Ownership: Accountable for team outcomes, platform health, and continuous improvement of engineering practices.
This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.
Estimated compensation for this position:212,000.00 - 259,000.00 USD per year
This range is an estimate and actual compensation may differ. Final compensation packages are determined by various considerations including but not limited to candidate qualifications, location, market conditions, and internal considerations.
Location:Remote -Boston, MA, Chicago, IL, Dallas, TX
If this job description resonates with you, we encourage you to apply, even if you don't meet all the requirements. We're interested in getting to know you and what you bring to the table!
Personalized benefits that support personal well-being and growth:JLL recognizes the impact that the workplace can have on your wellness, so we offer a supportive culture and comprehensive benefits package that prioritizes mental, physical and emotional health. Some of these benefits may include:
- 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
