Data Engineer

Global Ltd

Greater London, UK

7 days ago

Apply on www.collegerecruiter.com

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.collegerecruiter.com

Tech stack

Airflow Amazon Web Services Computer Programming Continuous Integration Data Validation Information Engineering Data Governance Data Infrastructure Data Systems Python (Programming Language) Standard Sql Data Processing

+6 more

Cloud Platform System Snowflake Infrastructure Automation Frameworks Machine Learning Operations Software Version Control Data Pipelines

Job description

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.

Requirements

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