Data Engineer

Global; Greater London; On-site; GBP 65,000 - 95,000; Full time

Job summary

Global is seeking a Data Engineer to design and build scalable data infrastructure powering AI-driven products and audience intelligence. The role focuses on batch and near real-time pipelines across ingestion, transformation and serving layers.

You will collaborate with Data Science, MLOps, Product and commercial teams, ensuring robust data quality, governance and clear documentation of data assets.

Qualifications

• Strong Python and SQL skills, with experience building production-grade data pipelines.
• Experience with modern data tools and cloud environments.
• Knowledge of CI/CD, testing, version control and IaC.
• Understanding of observability, validation and maintaining reliable data systems.
• Ability to translate business and data science needs into scalable solutions.

Responsibilities

• Data Platform & Pipeline Engineering (60%): Design, build and maintain scalable batch and near real-time pipelines across ingestion, transformation and serving layers.
• Platform Evolution & Engineering Excellence (20%): Shape data platform through best practices in architecture, tooling, CI/CD and infrastructure as code.
• Quality & Governance (10%): Implement data validation, testing, lineage and observability for high-quality datasets.
• Collaboration & Enablement (10%): Partner with Data Science, MLOps, Product and commercial teams to deliver production-ready data solutions.

Skills

Python

SQL

Data Pipelines

Data Platform

Tools

Snowflake

Airflow

dbt

AWS

Job description

Your role: Data Engineer

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

Accepting Applications Until

16 October 2026

Job Description

Your role: Data Engineer

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

Key Responsibilities

As a Data Engineer at Global, you will:

• 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