Data Engineer

Your role: Data Engineer

A hands-on role building scalable data infrastructure that powers AI-driven products and audience intelligence.

As a Data Engineer at Global, you will:

Key Responsibilities

• • Data Platform & Pipeline Engineering (60%): Design, build and maintain scalable batch and near real-time pipelines across ingestion, transformation and serving layers. Develop reusable data models and optimise performance, reliability and cost.
• • Platform Evolution & Engineering Excellence (20%): Shape the Global:IQ data platform through best practices in architecture, tooling, CI/CD and infrastructure as code. Create reusable components and maintain clear technical documentation.
• • Quality & Governance (10%): Implement robust data validation, testing, lineage and observability to ensure high-quality, trusted datasets. Support governance and privacy-conscious data handling.
• • Collaboration & Enablement (10%): Partner with Data Science, MLOps, Product and commercial teams to deliver production-ready data solutions. Support and mentor others while communicating clearly with stakeholders.

What You’ll Love About This Role

Think Big: Build a data platform from the ground up that will scale with a cutting-edge AI and ML product.

Own It: Take responsibility for production-grade data systems that directly power targeting, optimisation and measurement.

Keep it Simple: Apply pragmatic engineering to deliver reliable, maintainable solutions without over-engineering.

Better Together: Work in a highly collaborative, cross-functional team spanning technical and commercial expertise.

What Success Looks Like

In your first few months, you’ll have: • Developed a strong understanding of the Global:IQ platform and its core use cases • Successfully onboarded key datasets with robust ingestion and quality standards • Delivered reliable pipelines supporting live production use cases • Established or improved data engineering standards and best practices • Built strong working relationships across Data, Product and commercial teams • Identified opportunities to improve scalability, reliability and efficiency

What You'll Need

• • Programming & Data Skills: Strong Python and SQL skills, with experience building production-grade data pipelines
• • Data Platform Experience: Hands-on experience with modern data tools (e.g. Snowflake, Airflow, dbt) and cloud environments (preferably AWS)
• • Engineering Best Practice: Knowledge of CI/CD, testing, version control and infrastructure as code
• • Data Quality & Governance: Understanding of observability, validation and maintaining reliable data systems
• • Collaboration & Communication: Ability to translate business and data science needs into scalable solutions and communicate clearly with stakeholders
• • Mindset & Approach: Pragmatic, ownership-driven and curious, with a passion for building impactful data products