Principal Engineer

Cushman & Wakefield

12d•$157,250 - $185,000•Hybrid

About The Position

The Principal Data Engineer at Cushman & Wakefield is a senior, high-impact technical leader responsible for solving complex data engineering challenges and shaping enterprise-wide data architecture within the TDS Technology and Data Solutions team. Reporting to the Global Head of Data Architecture & Engineering, this role combines deep hands-on expertise with strategic influence—designing and implementing scalable data solutions on Databricks and the Azure ecosystem, establishing engineering standards, and driving architectural best practices. The role embeds across teams to accelerate delivery, mitigate risks, and ensure high-quality outcomes, while also mentoring engineers, promoting technical excellence, and acting as a trusted advisor to translate business needs into effective data strategies.

Requirements

Extensive data engineering experience at increasing levels of seniority, with a clear track record of delivering production-grade data platforms and pipelines at scale.
Near-expert hands-on proficiency with Databricks (Spark, Lakeflow, Spark Declarative Pipelines (DLT), Delta Lake, Lakebase/Postgres, Unity Catalog, etc.) and the Azure data ecosystem.
Demonstratable 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 and a strong instinct for reusable, scalable engineering patterns.

Nice To Haves

Experience embedding across multiple teams, geographies and time zones as a senior technical contributor or technical lead.
Familiarity with CI/CD and infrastructure-as-code tooling for data pipelines using Azure DevOps, Databricks Automation Bundles (DABS), GitHub Actions, or equivalent.
Good understanding of data governance, metadata management, and cataloguing in enterprise environments

Responsibilities

Take on the most technically demanding data engineering work – high-scale pipelines, performance-critical workloads, workload optimization, and platform-level capabilities on Databricks and Azure – where deep expertise is essential to success.
Write, review, and refactor production code that exemplifies the team’s standards for performance, reliability, security, observability, and cost efficiency.
Help define and continuously evolve the engineering and architectural principles, patterns, and reference implementations used across the Data Organization.
Maintain near-expert depth in Databricks and Azure data services and proactively build expertise in adjacent and emerging technologies on our 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 critical components of the architectures you design, ensuring they are demonstrably production ready.
Partner with the Architecture function to ensure designs align with enterprise standards for security, governance, scalability, and total cost of ownership.
Mentor data engineers across all levels through code reviews, pairing, design reviews, and direct coaching, with a particular focus on accelerating mid-level engineers toward senior contribution.
Lead internal tech talks and written deep dives on patterns, pitfalls, and platform capabilities to multiply the team’s expertise.
Embed into Data Engineering teams for engagements ranging from days to months to lead, accelerate, or de-risk critical projects, transferring expertise back to the host team upon exit.
Proactively identify technical risks, design flaws, and execution gaps, and drive issues to resolution with clear, well-reasoned recommendations.
Serve as the go-to technical voice for complex data problems and platform capabilities.
Translate business needs into clear technical strategies and translate technical trade-offs into language that supports informed decisions.
Collaborate with peers in Architecture, Platform, AI, Analytics, Security, and Governance to ensure data engineering work integrates cleanly into the broader data and technology landscape.