About the role

The Building Operations & Experience (BOE) Data Engineering Lead for CBRE acts as a global team leader for an experienced technical team delivering real estate data and analytics for CBRE's clients and internal stakeholders. Capabilities will be focused on data integration, data products, warehousing, visualization, and AI to optimize client portfolio operations across several lines of business: Enterprise, Local, and Property Management.

The BOE Data Engineering Lead will be responsible for leading all technical work by a global team of developers and QA professionals. S/he will implement the proper scalable technical solution within a multi-tenant environment. The solution with leverage CBRE's suite of products or developing custom solutions when necessary for data integration and transformation with a strong focus on on-time delivery and quality.

The role will collaborate with senior business and technical management, the product team, and dedicated account resources to ensure overall progress and stakeholder satisfaction. The ideal candidate possesses a broad skill set across several key categories: product management, team management, strong stakeholder-facing communications, vast technical capabilities in data engineering, software engineering, and AI, and ideally knowledge of the corporate real estate outsourcing industry.

What You'll Do
• Develop and own the data delivery/engineering strategy, aligned to digital and insights priorities, facilitating data intelligence and digital platforms.
• Own end-to-end data delivery within a multi-tenant real estate platform, ensuring scalability, reliability, and performance across a large enterprise client base.
• Operationalize GenAI and AI/ML use cases across the platform and client solutions.
• Collaborate with cross-functional teams, prioritize initiatives, lead multiple concurrent workstreams, including platform enhancements, data integrations, and client-specific custom solutions.
• Provide formal supervision to global, cross-functional teams (engineering, data, QA, and delivery), including coaching, performance management, and career development.
• Oversee continuous evaluation of digital systems, data pipelines, and processes to ensure alignment with business and client objectives.
• Balance platform standardization with client customization, ensuring extensibility while minimizing technical debt.
• Establish and manage delivery governance frameworks, including KPIs, SLAs, financial controls, and executive reporting.
• Maintain and manage department budgets, ensuring efficient allocation of resources and strong financial performance of digital platforms.
• Partner with product, engineering, and business stakeholders to define, prioritize and execute AI-first roadmap and technical strategy.
• Serve as a key member of the sales and client engagement lifecycle, supporting RFP responses, solution design, and client presentations.
• Support new client onboarding and platform implementations, ensuring seamless transitions and alignment on service delivery expectations.
• Act as a senior escalation point for complex delivery risks, issues, and client concerns, driving timely resolution.
• Coordinate system and platform strategies with field teams, accounting, and other business units to ensure integrated delivery.
• Identify and solve complex, multi-dimensional problems using data-driven and scalable approaches.
• Drive operational excellence through Agile/hybrid methodologies, automation, and CI/CD best practices.
• Lead by example and model behaviors that are consistent with CBRE RISE values, persuading managers and other colleagues to take action while being guided by the organization's functional business plans.

What You'll Need
• Bachelor's Degree, with advanced degree preferred. 8-12+ years of experience in digital technology, delivery management, or related fields (or equivalent combination of education and experience).
• Proven experience managing large-scale, multi-tenant SaaS platforms and enterprise analytics solutions.
• Strong expertise in data and analytics ecosystems, including data pipelines, BI/reporting, and cloud-based architectures (AWS, Azure, or GCP).
• Demonstrated success delivering complex, client-facing solutions at scale (multi-client / global environments).
• Experience leading globally distributed teams and managing cross-functional collaboration.
• Hands-on understanding of GenAI/LLMs (prompt engineering, RAG architectures, embeddings, vector databases) and experience with modern AI/data stack (Python, Spark, APIs, tools).
• Experience with MLOps /LLMOps frameworks (model lifecycle, evaluation, monitoring, governance).
• Ability to apply deep business and technical knowledge across multiple disciplines to influence outcomes.
• Exceptional communication skills with the ability to handle sensitive, complex information and manage senior stakeholders.
• Experience supporting sales pursuits, RFPs, and client solutioning is highly preferred.
• Strong financial acumen, including budgeting, forecasting, and cost optimization.
• Experience in AI/ML-driven analytics, modern AI tooling, and intelligent automation platforms.
• Excellent organizational, analytical, and problem-solving skills with a proactive, inquisitive mindset.

Technical Skills Required
• Cloud Platforms AWS, Azure, or GCP - including cloud-native storage, compute, and managed services.
• AI & Machine Learning GenAI/LLM experience (prompt engineering, RAG architectures, embeddings, MCP, vector databases, knowledge graphs), MLOps/LLMOps frameworks, and AI/ML-driven analytics and intelligent automation.
• Software & Data Development Python, Apache Spark, REST APIs, CI/CD pipelines, and Agile/hybrid delivery methodologies.
• BI, Visualization & Semantic Layer Building and governing BI/reporting solutions and semantic layers for self-service analytics.
• Delivery & Platform Management Managing SaaS platforms at enterprise scale, client onboarding, SLA governance, and KPI/financial reporting.
• Data Engineering & Architecture Design and management of scalable data pipelines including reviewing existing pipelines to identify opportunities for optimization, ETL/ELT processes, multi-tenant and single-tenant data platforms, and cloud-based data warehousing.