Data Engineer

• 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.

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.

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

• 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