About the role

Design, build, and maintain scalable data pipelines and platform capabilities to support the Digital Ad Exchange. Collaborate with cross-functional teams to deliver reliable data products and monitor production processes for quality and performance.

What they look for

SQL Python ETL/ELT AWS Databricks Apache Spark Terraform CI/CD Version Control Data Pipeline Design Data Modeling Observability

Requirements

Requires strong proficiency in SQL and Python for data engineering, along with experience in cloud platforms like AWS and Databricks. Candidates should have an engineering mindset focused on testing, CI/CD, and distributed processing using Apache Spark.

Full description

Accepting applications until:

14 August 2026

Job Description

Your Role: Data Engineer (DAX)

Global is looking for a Data Engineer to join the Data Engineering team supporting our Digital Ad Exchange (DAX).

You’ll design, build and maintain the data products, pipelines and platform capabilities that power DAX’s data-driven initiatives. Working closely with other Data Engineers and the wider Data Group, you’ll deliver reliable, well-tested and scalable data solutions, helping to shape and evolve our modern data platform and engineering practices.

This role reports to the Lead Data Engineer (DAX) and plays a key part in running a robust data platform that supports business insight, operational processes and product development across DAX.

As a Data Engineer (DAX) at Global, you will:

Key Responsibilities

Build & Delivery (50%): Design, develop and maintain data pipelines, services and platform capabilities used across DAX. Translate business and technical requirements into high-quality, maintainable solutions, writing clean, reliable and well-documented code for data ingestion, transformation and processing. Contribute to technical design, standards and best practices.

Collaboration & Team Contribution (20%): Work closely with Data Engineers, Analytics Engineers, Analysts, Product and other teams across DAX and the wider Data Group. Participate in code reviews, pairing and planning sessions, share knowledge with colleagues, and communicate clearly on progress, risks and technical decisions.

Operations, Support & Improvement (30%): Monitor and support the day-to-day operation of production pipelines and data processes. Lead or contribute to the investigation of pipeline failures, data quality issues and performance problems, improving observability, alerting and reliability, and deepening the team’s understanding of the DAX data domain.

What You’ll Love About This Role

Think Big: You’ll help drive the evolution of the DAX data platform, delivering data products and capabilities that support a key and growing part of the business.

Own It: You’ll take end-to-end ownership of data engineering workstreams, from design through to deployment and support.

Keep it Simple: You’ll build robust, scalable solutions that solve real engineering challenges in a practical, maintainable way.

Better Together: You’ll be part of a collaborative data team, working with talented engineers, analysts and data scientists to deliver meaningful business outcomes.

What Success Looks Like

In your first few months, you’ll have:

Gained a strong understanding of the DAX data platform, its architecture, data models, tooling and engineering practices.
Led or significantly contributed to the delivery and support of production data pipelines and services.
Demonstrated the ability to write clean, tested and maintainable code with minimal guidance.
Built confidence in diagnosing and resolving issues across data workflows and platform components.
Developed a solid grasp of the DAX data domain and how the platform supports commercial and product decisions.
Identified and delivered improvements to reliability, data quality, performance or developer experience.

What You’ll Need

SQL & Data Engineering: Strong SQL skills and solid experience working with relational data, ETL/ELT processes and data pipelines in production environments.
Programming Skills: Practical experience programming in Python for data engineering, with a focus on readable, maintainable and testable code.
Modern Data & Cloud Practices: Experience with modern data engineering tooling and cloud platforms (ideally AWS and Databricks), and familiarity with distributed processing (e.g. Apache Spark). Exposure to infrastructure as code (such as Terraform and Databricks Asset Bundles) is a plus.
Engineering Mindset: Understanding of testing, version control, CI/CD and observability, and a commitment to building reliable, secure and scalable data solutions.
Collaboration & Communication: Strong problem-solving skills, attention to detail and the ability to work effectively with technical and non-technical colleagues.
Curiosity & Impact: Interest in advertising technology, analytics or large-scale data systems, with a focus on how data engineering enables business outcomes.