Director, Data Engineering Delivery

Cushman & Wakefield is seeking a Director of Data Engineering Delivery to help shape data engineering strategy, standards, and execution across the organization. The role combines technical and people leadership, including managing a team of data engineers and analysts, overseeing delivery portfolios, building data platforms on Databricks and Azure, and partnering with global data and technology stakeholders.

Responsibilities:

• Platform ownership: Lead the design, build, and continuous improvement of scalable data pipelines, Lakehouse architectures, and data products on Databricks and Azure, ensuring our engineering and architectural standards for performance, reliability, and cost are consistently delivered
• Hands-on contribution: Remain an active practitioner by contributing to high-impact pipelines, code reviews, architecture reviews, and complex troubleshooting
• Quality and governance: Champion data quality, observability, security, and governance practices, embedding them into the team’s engineering lifecycle and platform standards
• Team management: Directly manage a team of 5–8 data engineers, owning hiring, onboarding, performance management, compensation recommendations, and retention
• Coaching and development: Provide regular coaching, feedback, and career development planning for each team member, with clear growth paths for both individual contributor and leadership tracks
• Culture and engagement: Foster an inclusive, high-trust team culture grounded in psychological safety, technical curiosity, accountability, and continuous learning
• Delivery ownership: Plan, prioritize, and orchestrate the team’s portfolio of data engineering work, ensuring on-time, on-quality delivery aligned with global data and business priorities
• Agile execution: Establish and refine agile delivery practices (intake, estimation, sprint planning, retrospectives) that balance discovery work, platform investment, and run-the-business commitments
• Risk and dependency management: Proactively identify, communicate, and resolve risks, blockers, and cross-team dependencies, escalating clearly and constructively when needed
• Operational excellence: Own production health for the team’s data products, including SLAs, on-call posture, incident response, and post-incident learning
• Business partnership: Build trusted relationships with business and technology stakeholders, translating their objectives into clearly scoped, prioritized data engineering outcomes
• Global team collaboration: Partner closely with peers across the Global Data Leadership team - architecture, data engineering, AI, governance, and product - to deliver cohesive, end-to-end data solutions
• Communication and storytelling: Communicate technical concepts, trade-offs, roadmaps, and progress effectively to audiences ranging from engineers to senior executives