WPP is the trusted growth partner for the world’s leading brands.

We unite cutting-edge media intelligence and data solutions, world-class creativity, next-generation production, transformative enterprise solutions and expert strategic counsel in a single company – powered by exceptional talent and our agentic marketing platform, WPP Open, to help our clients navigate change, capture opportunity and deliver transformational growth.

We work with the world's most valuable brands and have global reach across 100+ markets, with deep local expertise.

Our people are the key to our success. We're committed to fostering a culture of creativity, belonging and continuous learning, attracting and developing the brightest talent, and providing exciting career opportunities that help our people grow.

For more information, visit WPP.com.

Why We’re Hiring

We are scaling the core engineering capability behind real-time, high-throughput ad-tech infrastructure that processes large-scale audience data at global scale.

This is not a role for basic ETL scripts or glue code. In this role, data engineering is treated as a true software engineering discipline. You will take deep technical ownership of complex, multi-layered data models (canonical schemas, high-dimensional behavioral signals, and vector embeddings) that power live targeting and measurement decisions for advertisers worldwide.

You’ll help evolve a distributed, production-grade data platform while collaborating with a high-trust engineering team that values clean architecture, continuous improvement (kaizen), and modern development standards.

Who You’ll Be Working With

You’ll work closely with fellow engineers across backend systems, Quality Assurance, Analytics, and connector teams. We value teamwork over ego, collaboration over competition, open and honest communication, strong opinions held with curiosity, and delivering well-tested, maintainable code.

Our Technology Environment

Languages & Core: Scala, functional programming, strongly typed distributed services
Processing & Streaming: Apache Spark for large-scale distributed processing, event-driven architectures
Cloud & Storage: Google Cloud Platform (GCP), BigQuery, Parquet
Engineering Standards: Automated CI/CD, robust unit & integration testing, Git workflows, and Tyda (our open-source, automation-driven SDLC process)

What You’ll Do

Own and evolve production data models spanning input/output models, schema evolution, field types, and partitioning strategies across multi-stage distributed pipelines.
Maintain and extend our audience framework, normalizing high-velocity raw inputs into standardized, canonical formats across behavioral signals, demographics, and brand affinity.
Design and manage models handling relational structures, real-time behavioral signals, high-dimensional embeddings, and anomaly detection.
Develop and maintain Spark-based data services in Scala and GCP, adhering to software design patterns, modular architecture, and edge-case resilience.
Lead by example with automated unit/integration testing, strict code reviews, Git branching discipline, and CI/CD best practices.
Partner with QA, Analytics, and downstream product teams to guarantee contract integrity, data correctness, and reliable system consumption.

What You’ll Need

3+ years of software engineering or data engineering experience, with a proven track record of shipping production-grade distributed data systems.
Production experience writing quality code in Scala OR another statically typed language (Java, Kotlin, C#, Go). If your background is in Java/Kotlin/Go, you are eager and excited to build in a Scala-first environment.
Hands-on experience collaborating on large codebases with automated testing (unit/integration testing), version control (Git), CI/CD pipelines, and structured code reviews.
Proven ability to design and reason about complex, multi-layered data architectures (e.g., canonical schemas, dimensional modeling, Data Vault, or domain-driven data design).
Solid experience with Apache Spark (or similar distributed frameworks) handling high-volume datasets.