Principal Engineer

Description

This senior principal role sits in the TDS Technology and Data Solutions department and reports to the Global Head of Data Architecture & Engineering.

You will be one of the most senior data engineers on the team and a near-expert practitioner in the field, solving the hardest data engineering problems and shaping data architecture with influence across the Data Organization.

You will build scalable data solutions on Databricks and Azure, set engineering standards, and mentor engineers at every level. You will also move between Data Engineering teams, working in projects for days or months at a time.

The primary platform is Databricks and Azure. You will evaluate and adopt new data technologies as the platform evolves.

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

Responsibilities

• Take on the hardest data engineering work, such as high-scale pipelines, performance-critical workloads, workload tuning, and platform capabilities on Databricks and Azure.
• Design end-to-end architectures spanning ingestion, storage, transformation, serving, and governance, using Lakehouse patterns.
• Implement and validate the critical parts of the architectures you design, so they are proven production ready.
• Write, review, and refactor production code to the team's standards for performance, reliability, security, observability, and cost.
• Work with the Architecture function so designs meet enterprise standards for security, governance, scalability, and total cost of ownership.
• Help define and evolve the engineering and architectural principles, patterns, and reference implementations used across the Data Organization.
• Stay near expert in Databricks and Azure data services, and build skills in adjacent and emerging technologies on the roadmap.
• Mentor engineers at all levels through code reviews, pairing, design reviews, and 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 features.
• Embed in Data Engineering teams for days to months to lead, speed up, or de-risk key projects, then pass expertise back to the host team when you leave.
• Spot technical risks, design flaws, and delivery gaps early, and drive them to resolution with clear recommendations.
• Act as the go-to technical voice for complex data problems and platform capabilities.
• Turn business needs into technical strategies, and explain trade-offs in plain terms so decision makers can choose well.
• Work with peers in Architecture, Platform, AI, Analytics, Security, and Governance so data engineering fits the wider technology landscape.

Requirements

• Years of data engineering work at growing levels of seniority, with a proven record of delivering production data platforms and pipelines at scale.
• Near-expert hands-on skill with Databricks, including Spark, Lakeflow, Spark Declarative Pipelines (DLT), Delta Lake, Lakebase/Postgres, Unity Catalog, and similar tools, plus the Azure data ecosystem.
• A demonstrable record of designing and implementing end-to-end data architectures for complex, enterprise-scale environments.
• A track record of mentoring engineers and raising team-wide standards through influence, without direct reports.
• Familiarity with Lakehouse, medallion, and data mesh patterns, DataOps practices, and metadata- and configuration-driven pipeline frameworks, plus a strong instinct for reusable, scalable 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, with a US/EMEA working time zone.

Career level P7.

Compensation for this role is $157,250.00 - $185,000.00.

Pay depends on whether the role is covered by a collective bargaining agreement, the work location, local market rates, and your experience and qualifications.

Eligible employees receive competitive pay, which can vary by location, hire date, total hours worked, job type, business line, and collective bargaining agreements.

Pay will never fall below minimum wage for this role.

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

Benefits

• Eligible employees can enrol 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.