Sr Manager, Data Engineering (Remote)

A property management organization is seeking a new Senior Manager, Data Engineering to lead platform engineering and team leadership efforts in a remote role aligned to U.S. Eastern or Central time. This position oversees the buildout of scalable data solutions, hands-on Databricks delivery, and the development of a high-performing data engineering team.

About the Opportunity:

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  Shift: Day shift

  
  


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  Schedule: Monday-Friday

  
  


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  Hours: 9:00 AM-5:00 PM, flexible depending on project and time zone needs

  
  


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  Setting: Remote

  
  

Responsibilities:

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  Lead the design, build, and continuous improvement of scalable data pipelines, Lakehouse architectures, and data products.

  
  


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  Contribute hands-on to high-impact engineering work, including code reviews, architecture reviews, and complex troubleshooting.

  
  


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  Provide technical leadership across data quality, observability, security, governance, and platform standards.

  
  


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  Directly manage and develop a team of data engineers, including hiring, onboarding, coaching, performance management, and retention.

  
  


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  Plan, prioritize, and coordinate delivery across the team while managing risks, dependencies, and cross-functional stakeholder expectations.

  
  

Qualifications:

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  Significant data engineering experience with hands-on Databricks and Azure platform delivery.

  
  


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  Experience formally managing data engineers, including hiring, performance management, and career development.

  
  


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  Track record of delivering production-grade data platforms and pipelines at scale.

  
  


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  Experience leading technical work through influence in a global, matrixed environment.

  
  


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  Strong communication, stakeholder management, and prioritization skills.

  
  

Desired Skills:

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  Experience with commercial real estate or professional services environments.

  
  


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  Familiarity with Lakehouse, Unity Catalog, medallion, and data mesh architecture patterns.

  
  


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  Experience with DataOps practices and metadata-driven or configuration-driven pipeline frameworks.

  
  


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  Exposure to CI/CD and infrastructure-as-code tooling such as Azure DevOps, Databricks Automation Bundles, or GitHub Actions.

  
  


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  Experience working across multiple geographies and time zones.