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# Senior Manager, Data Engineering

- **Company:** Cushman & Wakefield
- **Location:** St. Louis, MO, United States
- **Experience:** Expert
- **Salary:** $140,250.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Continuous Integration, Information Engineering, Github, PostgreSQL, Metadata, DataOps, Azure Data Factory, Apache Spark, Data Lakes, Data Management, Data Pipelines, Databricks
- **Published:** October 16, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3451614828&tx=HT8478THD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

authority, Excellent communication, stakeholder management, and prioritization skills in a global, matrixed environment, with a client-service mindsetFamiliarity with modern data architecture patterns (Lakehouse, Unity Catalog, medallion, data mesh), DataOps practices, and metadata-driven and configuration-driven pipeline frameworks. Desirable Skills, Knowledge & Experience: < >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 equivalentExperience leading cross-team initiatives and working across multiple geographies and time zones as part of a global data organization. This job description is intended to outline the primary responsibilities and requirements of the role. It is not exhaustive and may be subject to change in line with organisational

## Description

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 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