Senior Manager, Data Engineering

Description

The role is a senior manager position in data engineering within the technology and data solutions department, reporting to the global head of data architecture and engineering.

The job is hybrid or remote depending on location, and the working time zone is US or EMEA.

The company is a global commercial real estate services firm with about 52,000 employees in nearly 400 offices across 60 countries, and it reported revenue of $9.5 billion in 2023. Its core services are property, facilities and project management, leasing, capital markets, and valuation.

The role is a hybrid of technical and people leadership: you keep building data engineering work on Databricks and Azure while leading, mentoring, and growing a team of 5 to 8 data engineers and analysts.

You will sit on the global data leadership team and help shape data engineering strategy, standards, and delivery across the organization. You need working fluency with Databricks and the Azure ecosystem now, and the ability to learn and adopt other data technologies as the platform changes.

Career level: M6.

Responsibilities

• Design, build, and continuously improve scalable data pipelines, Lakehouse architectures, and data products on Databricks and Azure, meeting the engineering and architecture standards for performance, reliability, and cost.
• Stay hands-on by contributing to high-impact pipelines, code reviews, architecture reviews, and complex troubleshooting.
• Build data quality, observability, security, and governance into the team's engineering lifecycle and platform standards.
• Manage a team of 5 to 8 data engineers directly, including hiring, onboarding, performance management, compensation recommendations, and retention.
• Coach each team member and plan their development, with clear growth paths on both the individual contributor and leadership tracks.
• Build an inclusive, high-trust team culture based on psychological safety, technical curiosity, accountability, and continuous learning.
• Plan, prioritize, and manage the team's data engineering work so it is delivered on time and to quality, in line with global data and business priorities.
• Set up and refine agile practices for intake, estimation, sprint planning, and retrospectives that balance discovery, platform investment, and run-the-business work.
• Identify risks, blockers, and cross-team dependencies early, communicate them, and resolve them, escalating clearly when needed.
• Own production health for the team's data products, including SLAs, on-call coverage, incident response, and post-incident reviews.
• Build trusted relationships with business and technology stakeholders, turning their goals into clearly scoped, prioritized data engineering outcomes.
• Work closely with peers across the global data leadership team, including architecture, data engineering, AI, governance, and product, to deliver end-to-end data solutions.
• Explain technical concepts, trade-offs, roadmaps, and progress clearly to audiences 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.
• Formal experience managing data engineers, including hiring, performance management, and career development.
• A track record of delivering production-grade data platforms and pipelines at scale, with strong attention to reliability, security, and cost.
• The ability to lead complex technical work through influence rather than 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.
• Desirable: experience leading teams through technology transitions and adopting new data tooling beyond an established core stack.
• Desirable: familiarity with CI/CD and infrastructure-as-code tooling for data pipelines, such as Azure DevOps, Databricks Automation Bundles (DABS), GitHub Actions, or equivalent.
• Desirable: experience leading cross-team initiatives and working across multiple geographies and time zones within a global data organization.

Conditions

The working time zone is US or EMEA, and the work is hybrid or remote depending on location.

Pay is $140,250.00 to $165,000.00. The final offer depends on whether the role is covered by a collective bargaining agreement, the geographic area, local market rates, and the candidate's experience and qualifications.

The company will not pay less than minimum wage for this role.

The job description covers the main duties and requirements and is not exhaustive. It may change as the organization's needs change.

The company is an equal opportunity employer and does not tolerate discrimination.

Benefits

• Eligible employees can enroll in health, vision, and dental insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance, and paid and unpaid time away from work.
• Pay may vary for eligible employees based on factors such as location, hire date, total hours worked, job type, business line, and collective bargaining agreements.