Pay
Lower than most similar jobs
$140K–$165K against $154K–$225K, the middle half of what similar US postings state.
From 2,958 postings in the last year. Stated ranges, not salaries paid.
Reads like a Data Leadership role: running a data team.
Remote CW Site - USA - Flexible United States
Live, first seen by us 22 days agoThe company will not pay less than minimum wage for this role. The compensation for the position is: $ 140,250.00 - $165,000.00 Cushman & Wakefield is an Equal Opportunity employer to all protected groups, including protected veterans and individuals with disabilities. Discrimination of any type will not be tolerated.Read the full posting
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.
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.
Listed location: Remote CW Site - USA - Flexible United StatesRead the full posting
Employment type in the employer’s posting: Full timeFrom the employer’s posting
We are seeking a Manager of Data Engineering who is equally comfortable engineering data platforms and developing the engineers who build on them.
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.
Design and improve scalable data pipelines and Lakehouse architectures on Databricks and Azure
Hire, coach and manage performance for a team of 5–8 data engineers
Contribute hands-on to pipelines, code reviews and complex troubleshooting
Plan and prioritize the team's data engineering work and agile delivery
Own production health, SLAs, on-call and incident response for team data products
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.
Lead the design, build, and continuous improvement of scalable data pipelines, Lakehouse architectures, and data products on Databricks and Azure
Directly manage a team of 5–8 data engineers, owning hiring, onboarding, performance management, compensation recommendations, and retention.
Remain an active practitioner by contributing to high-impact pipelines, code reviews, architecture reviews, and complex troubleshooting.
Plan, prioritize, and orchestrate the team’s portfolio of data engineering work
Own production health for the team’s data products, including SLAs, on-call posture, incident response, and post-incident learning.
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.
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
< >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 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 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.
< >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 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 needs.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Lower than most similar jobs
$140K–$165K against $154K–$225K, the middle half of what similar US postings state.
From 2,958 postings in the last year. Stated ranges, not salaries paid.
Day 22
Similar jobs usually come down between day 7 and day 54.
From 8,108 similar postings that came down. Coming down is not always a hire.
14 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Check the employer’s posting for the current role and application details.
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New Data Leadership postings at the employers we follow are up 7% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
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 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 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 needs.
We foster a culture of inclusion that embraces the unique strengths, perspectives, and experiences of all our employees. We firmly believe that our diversity enhances our team's capabilities, leading to improved decision-making, innovation, and business outcomes. If you have any reservations about applying, please don't hesitate to reach out to your local recruiter for additional information
In compliance with the Americans with Disabilities Act Amendments Act (ADAAA), if you have a disability and would like to request an accommodation in order to apply for a position at Cushman & Wakefield, please call the ADA line at 1-888-365-5406 or email Accommodations@cushwake.com. Please refer to the job title and job location when you contact us.
INCO: “Cushman & Wakefield”Selected passages from the saved posting. Check the full description for conditions and exceptions.
The company will not pay less than minimum wage for this role. The compensation for the position is: $ 140,250.00 - $165,000.00 Cushman & Wakefield is an Equal Opportunity employer to all protected groups, including protected veterans and individuals with disabilities. Discrimination of any type will not be tolerated.
Remote CW Site - USA - Flexible United States
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.
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.
No clear work-authorization passage found. Eligibility is unconfirmed.
We couldn’t identify enough role detail in this saved description to support an AI answer. Read the full posting