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We are hiring a Data Engineer for a data, services, and software company that partners with governments, cities, fitness operators, and sports bodies to maximise return on investment in sport, leisure,e and physical activity programmes. The Data Engineer will play a key role in building and maintaining reliable data pipelines and integrations that feed InsightOS. This position supports the internalisation of data engineering capabilities previously handled externally, enabling stronger control over data quality, reliability, and performance. Working closely with Backend, GIS, and Infrastructure colleagues, you will help design scalable ingestion and transformation workflows that underpin our predictive analytics platform.
Job Responsibility:
Develop and maintain ETL/ELT pipelines to integrate data from multiple internal and external sources into InsightOS
Support API-driven data ingestion and transformation workflows, including monitoring and troubleshooting
Collaborate with Backend, GIS, and Infrastructure teams to ensure consistent, reliable data availability for product and internal stakeholders
Improve data quality, performance, and documentation across pipelines and integrations
Contribute to defining data models, ingestion strategies, and best practices for transformation and validation
Ensure alignment with 4GLOBAL’s data governance, reliability, and security standards
Provide day-to-day technical leadership,including mentoring/supporting more junior employee’s, contributing to delivery planning, and helping to drive best practice across the team
Requirements:
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Statistics, Mathematics, or a related field (or equivalent practical experience)
A minimum of 5 years’ experience in data engineering (or closely related roles), with demonstrable ownership of production-grade pipelines and integrations
Familiarity with cloud environments and data infrastructure concepts (e.g., storage, compute, orchestration, monitoring)
Strong SQL fundamentals with experience querying and transforming complex datasets
Hands-on experience with Python (or a similar language/tooling) for data processing and automation
Solid understanding of ETL/ELT concepts, data modelling, and data pipeline design patterns
Experience ingesting data via APIs (authentication, rate limits, data normalisation, error handling)
Strong problem-solving skills and the ability to diagnose data issues and optimise pipeline performance
Management/leadership experience, such as mentoring, coaching, leading workstreams, or line management responsibility within an engineering or data context
Clear communication skills and comfort working cross-functionally in a fast-moving environment
Fluent English (spoken and written) is essential
Nice to have:
A passion for sports, with an understanding of sports participation trends and concepts, is desirable