Senior Manager, Data Engineering

Cushman & Wakefield is a leading global commercial real estate services firm, and they are seeking a Senior Manager of Data Engineering. This role involves both technical leadership in data engineering and people management, focusing on building scalable data platforms and mentoring a team of data engineers.

Responsibilities:

• 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
• Remain an active practitioner by contributing to high-impact pipelines, code reviews, architecture reviews, and complex troubleshooting
• Champion data quality, observability, security, and governance practices, embedding them into the team’s engineering lifecycle and platform standards
• Directly manage a team of 5–8 data engineers, owning hiring, onboarding, performance management, compensation recommendations, and retention
• Provide regular coaching, feedback, and career development planning for each team member, with clear growth paths for both individual contributor and leadership tracks
• Foster an inclusive, high-trust team culture grounded in psychological safety, technical curiosity, accountability, and continuous learning
• 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
• Establish and refine agile delivery practices (intake, estimation, sprint planning, retrospectives) that balance discovery work, platform investment, and run-the-business commitments
• Proactively identify, communicate, and resolve risks, blockers, and cross-team dependencies, escalating clearly and constructively when needed
• Own production health for the team’s data products, including SLAs, on-call posture, incident response, and post-incident learning
• Build trusted relationships with business and technology stakeholders, translating their objectives into clearly scoped, prioritized data engineering outcomes
• 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
• Communicate technical concepts, trade-offs, roadmaps, and progress effectively to audiences ranging from engineers to senior executives

Requirements:

• 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 engineers, including hiring, performance management, and career development
• Track record of delivering production-grade data platforms and pipelines at scale with strong attention to reliability, security, and cost
• Demonstratable ability to lead complex technical work through influence, not authority
• Excellent communication, stakeholder management, and prioritization skills in a global, matrixed environment, with a client‑service mindset
• Familiarity with modern data architecture patterns (Lakehouse, Unity Catalog, medallion, data mesh), DataOps practices, and metadata-driven and configuration-driven pipeline frameworks
• Experience leading teams through technology transitions and adopting new data tooling beyond an established core stack
• Familiarity with CI/CD and infrastructure-as-code tooling for data pipelines using Azure DevOps, Databricks Automation Bundles (DABS), GitHub Actions, or equivalent
• Experience leading cross‑team initiatives and working across multiple geographies and time zones as part of a global data organization