Senior Manager, Data Engineering

• Job Title • Senior Manager, Data Engineering • Job Description Summary • Job Description • Job Title: Data Engineering Senior Manager • Position: Data Engineering Senior Manager • Department: TDS Technology and Data Solutions • Reports To: Global Head of Data Architecture & Engineering • Location: Hybrid/Remote depending on location, Working time zone: US/EMEA Cushman & Wakefield (NYSE: CWK) is a leading global commercial real estate services firm for property owners and occupiers with approximately 52,000 employees in nearly 400 offices and 60 countries. In 2023, the firm reported revenue of $9.5 billion across its core services of property, facilities and project management, leasing, capital markets, and valuation and other services. It also receives numerous industry and business accolades for its award-winning culture and commitment to Diversity, Equity and Inclusion (DEI), sustainability and more. For additional information, visit www.cushmanwakefield.com. • Career Level: M6 • Role Summary: • We are seeking a Manager of Data Engineering who is equally comfortable engineering data platforms and developing the engineers who build on them. This is a hybrid technical and people leadership role: the successful candidate will continue to contribute hands-on to data engineering deliverables on Databricks and Azure while also leading, mentoring, and growing a team of 5–8 data engineers and analysts. • Reporting to the Global Head of Data Architecture & Engineering, the Data Engineering Manager will be a member of the Global Data Leadership team and will help shape data engineering strategy, standards, and execution across the organization. The role requires fluency with Databricks and the Azure ecosystem today, and the adaptability to evaluate and adopt additional data technologies as our platform evolves. • Key Responsibilities: • Technical Leadership • 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. • People Leadership • 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. • Project and Work Orchestration • 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. • Stakeholder Management and Cross Team Collaboration • 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.Essential Skills, Knowledge & Experience: Significant data engineering experience, including hands-on delivery on Databricks (Spark, Lakeflow, Spark Declarative Pipelines (DLT), Delta Lake, Lakebase/Postgres, Unity Catalog, etc.) and the Azure data ecosystem.Demonstratable experience formally managing data engin

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