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# Principal Data Engineer

- **Company:** Cushman & Wakefield
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $157,250.0 - $185,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Code Review, Encodings, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Systems, Github, PostgreSQL, Meta-Data Management, DataOps, Technical Data Management Systems, Azure Data Factory, Apache Spark, Data Strategy, Data Lakes, Production Code, Data Management, Data Pipelines, Databricks
- **Published:** July 1, 2026
- **Apply:** https://www.dice.com/job-detail/9bf91443-0b4f-450a-955c-d661fc9df7a5

## About the Role

< >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.Desirable Skills, Knowledge & Experience:

< >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

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.

## Description

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., We are seeking a Principal Data Engineer to serve as one of the most senior data engineers on our team and a near-expert practitioner in the domain. This role combines deep technical execution with broad influence: tackling the most complex data engineering problems, designing and implementing data architectures, and raising the bar for engineering excellence across the Data Organization.

Reporting to the Global Head of Data Architecture & Engineering, the Principal Data Engineer is expected to move fluidly across Data Engineering teams - embedding in projects for days or months at a time to unblock, accelerate, and uplift delivery. They will mentor engineers at every level, push standards for technical excellence, and help establish the engineering and architectural principles that guide our work. The primary platform is Databricks and the Azure cloud ecosystem, with the expectation of evaluating and adopting additional data technologies as the platform evolves.

Key Responsibilities:

Technical Data Engineering Execution

< >Engineering delivery: 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.Engineering excellence: Write, review, and refactor production code that exemplifies the team's standards for performance, reliability, security, observability, and cost efficiency.Standards and principles: Help define and continuously evolve the engineering and architectural principles, patterns, and reference implementations used across the Data Organization.Continuous learning: Maintain near-expert depth in Databricks and Azure data services and proactively build expertise in adjacent and emerging technologies on our roadmap.Data Architecture

< >Architecture design: Design end-to-end data architectures - covering ingestion, storage, transformation, serving, and governance - using Lakehouse patterns on Databricks and the Azure data ecosystem.Implementation ownership: Implement and validate critical components of the architectures you design, ensuring they are demonstrably production ready.Architectural alignment: Partner with the Architecture function to ensure designs align with enterprise standards for security, governance, scalability, and total cost of ownership.People Mentorship

< >Engineer development: 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.Knowledge sharing: Lead internal tech talks and written deep dives on patterns, pitfalls, and platform capabilities to multiply the team's expertise.Project Leadership

< >Cross-team embedment: 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.Risk and quality oversight: Proactively identify technical risks, design flaws, and execution gaps, and drive issues to resolution with clear, well-reasoned recommendations.Stakeholder Management

< >Trusted technical advisor: Serve as the go-to technical voice for complex data problems and platform capabilities.Translation and influence: Translate business needs into clear technical strategies and translate technical trade-offs into language that supports informed decisions.Cross-functional partnership: 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.Essential Skills, Knowledge & Experience


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