Principal Engineer

Description

The company is a global commercial real estate services firm serving property owners and occupiers. It has about 52,000 employees, nearly 400 offices and operates in 60 countries. It is NYSE-listed and reported $9.5 billion in revenue for 2023. Its core services are property, facilities and project management, leasing, capital markets, and valuation and other services.

This is a P7 principal role in the TDS Technology and Data Solutions department, reporting to the Global Head of Data Architecture & Engineering.

You will be one of the most senior data engineers on the team and close to an expert in the field. You will combine hands-on delivery with wide influence, tackling the hardest data engineering problems, designing data architectures and raising engineering standards across the Data Organization.

You will move between Data Engineering teams, embedding in projects for days or months to unblock and speed up delivery. The main platform is Databricks and the Azure cloud, and you will be expected to assess and adopt further data technologies as the platform changes.

Responsibilities

• Take on the most technically demanding data engineering work, such as high-scale pipelines, performance-critical workloads, workload tuning and platform capabilities on Databricks and Azure.
• Write, review and refactor production code to the team's bar for performance, reliability, security, observability and cost.
• Help define and keep improving the engineering and architectural principles, patterns and reference implementations across the Data Organization.
• Keep near-expert knowledge of Databricks and Azure data services, and build skills in adjacent and emerging technologies on the roadmap.
• Design end-to-end architectures covering ingestion, storage, transformation, serving and governance, using Lakehouse patterns on Databricks and Azure.
• Build and validate the critical parts of your architectures so they are proven to be production ready.
• Work with the Architecture function so 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 coaching, with a focus on helping mid-level engineers reach 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, speed up or de-risk critical projects, then pass the expertise back to the host team.
• Spot technical risks, design flaws and execution gaps early, and drive them to resolution with clear 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 so people can make informed decisions.
• Work with peers in Architecture, Platform, AI, Analytics, Security and Governance so data engineering fits the wider data and technology landscape.

Requirements

• Extensive data engineering experience at rising levels of seniority, with a proven 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.
• Demonstrated 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 data 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 time zone: US/EMEA.

This job description outlines the primary responsibilities and requirements, is not exhaustive, and may change in line with organisational needs.

The company is an equal opportunity and affirmative action employer and considers all qualified candidates regardless of protected characteristics.