Senior Manager, Data Engineering
About The Position
Cushman & Wakefield (NYSE: CWK) is a leading global commercial real estate services firm. 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.
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.
- Demonstrable 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.
- Demonstrable 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.
Nice To Haves
- 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.
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.
Benefits
- health, vision, and dental insurance
- flexible spending accounts
- health savings accounts
- retirement savings plans
- life, and disability insurance programs
- paid and unpaid time away from work
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
