Principal Engineer

Description

The principal data engineer is a senior technical leader who tackles the hardest data engineering problems and shapes data architecture across the enterprise within the TDS Technology and Data Solutions team.

The role reports to the Global Head of Data Architecture & Engineering and mixes hands-on engineering with strategic influence, building scalable data solutions on Databricks and the Azure ecosystem, setting engineering standards and steering architectural practice.

The role is hybrid or remote depending on location, with a US/EMEA working time zone.

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

This is a P7 career level role, one of the most senior data engineering positions on the team.

The principal engineer works across Data Engineering teams, embedding in projects for days or months to unblock and speed up delivery, mentors engineers at every level, and helps set the engineering and architectural principles the team follows. Databricks and Azure are the primary platform, and the role includes evaluating and adopting new data technologies as the platform evolves.

Responsibilities

• Take on the most demanding data engineering work, such as high-scale pipelines, performance-critical workloads, workload optimization and platform-level capabilities on Databricks and Azure, where deep expertise is essential.
• Write, review and refactor production code that meets the team's standards for performance, reliability, security, observability and cost efficiency.
• Help define and keep evolving the engineering and architectural principles, patterns and reference implementations used across the Data Organization.
• Keep near-expert depth in Databricks and Azure data services, and build expertise in adjacent and emerging technologies on the roadmap.
• Design end-to-end data architectures covering ingestion, storage, transformation, serving and governance, using Lakehouse patterns on Databricks and the Azure data ecosystem.
• Implement and validate the critical components of the architectures you design, making sure they are demonstrably production ready.
• Work with the Architecture function to make sure designs meet enterprise standards for security, governance, scalability and total cost of ownership.
• Mentor data engineers at all levels through code reviews, pairing, design reviews and direct coaching, with a focus on moving mid-level engineers toward senior contribution.
• Lead internal tech talks and written deep dives on patterns, pitfalls and platform capabilities.
• Embed in Data Engineering teams for days to months to lead, accelerate or de-risk critical projects, and pass expertise back to the host team when you leave.
• Spot technical risks, design flaws and execution gaps early, and drive them to resolution with clear, well-reasoned recommendations.
• Be the go-to technical voice for complex data problems and platform capabilities.
• Turn business needs into clear technical strategies, and explain technical trade-offs in terms that support informed decisions.
• Work with peers in Architecture, Platform, AI, Analytics, Security and Governance so data engineering work fits the wider data and technology landscape.

Requirements

• Extensive data engineering experience at increasing levels of seniority, with a clear record of delivering production-grade data platforms and pipelines at scale.
• Near-expert hands-on skill with Databricks (Spark, Lakeflow, Spark Declarative Pipelines (DLT), Delta Lake, Lakebase/Postgres, Unity Catalog, etc.) and the Azure data ecosystem.
• Demonstrable experience designing and implementing end-to-end data architectures for complex, enterprise-scale environments.
• Proven ability to mentor engineers, lead through influence without direct reports, and raise team-wide standards for technical excellence.
• Familiarity with modern architecture patterns (Lakehouse, medallion, data mesh), DataOps practices, and metadata-driven and configuration-driven pipeline frameworks, plus a strong instinct for reusable, scalable engineering patterns.
• Desirable: experience embedding across multiple teams, geographies and time zones as a senior technical contributor or technical lead.
• 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: good understanding of data governance, metadata management and cataloguing in enterprise environments.

Conditions

Hybrid or remote depending on location, working US/EMEA time zones.

The compensation range is $157,250.00 to $185,000.00. Final pay depends on collective bargaining coverage, the work location's geographic area, local market rates, and the candidate's experience and qualifications.

Pay will never fall below the applicable minimum wage for this role.

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

The company is an equal opportunity employer covering all protected groups, including protected veterans and people with disabilities, and does not tolerate discrimination.

Benefits

• Eligible employees can enrol in benefit programs that generally include 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 is competitive and may vary with eligibility factors such as geographic location, date of hire, total hours worked, job type, business line and collective bargaining agreements.