Data Engineer

Our Story In July 2019, Fourth joined forces with Hot Schedules to become the global leader in end-to-end restaurant and hospitality management technology solutions. Together, the merged company now represents the world’s largest and only provider of end-to-end restaurant and hospitality management solutions for customers across the globe and of all sizes, from a single location or franchisee restaurant to a global restaurant or hotel chain.

The combined company’s complete software-as-service (SaaS) solution suite including scheduling, time & attendance, applicant tracking, training, inventory management / procurement, HR / benefits and payroll services now serves customers in 120,000 locations worldwide and is supported by a dedicated, unified team across offices in the US, UK, Bulgaria, China, Australia, and UAE. Interested in joining our smart, fun, and talented team?

Position Overview As a Data Engineer on the Enterprise Data & Intelligence team, you will design, build, and maintain the data pipelines and models that underpin Fourth’s single source of truth. You will gather and translate business requirements into reliable, well-tested data solutions on our Azure-based cloud platform. You will collaborate closely with analytics engineers and business stakeholders to scope, prioritise, and deliver data products that serve FP&A, Commercial, Product, and AI teams.

As Fourth moves towards an AI-native operating model, you will play a key role in building and maintaining the data infrastructure that enables AI-powered analytics and decision-making across the business.

Responsibilities:

Data Pipeline Development & Optimisation
• Build, maintain, and optimise scalable ETL/ELT pipelines (batch and near-real-time) on Azure Data Cloud Platform (e.g., Data Lake, Microsoft Fabric, Azure Data Factory).
• Develop and refine data models to support BI reporting, analytics, and ML/AI use cases.
• Write efficient, well-documented T-SQL and PySpark code following team coding standards.

Data Quality, Testing & Governance
• Implement automated testing, data validation, and monitoring (SLAs, alerts) to ensure pipeline reliability.
• Contribute to data governance practices, including lineage tracking, metadata management, and quality controls.
• Support CI/CD pipelines for data assets, ensuring version control and reproducibility.

Requirements Scoping & Cross-Functional Collaboration
• Partner with Analytics Engineers to scope, refine, and prioritise data requirements from business stakeholders.
• Work with Analysts, BI Developers, Data Scientists, and business teams to translate requirements into production-ready data solutions.
• Provide input on data readiness for machine learning and analytics projects.

Platform Contribution & Continuous Improvement
• Contribute to the evolution of the data platform, including tooling, standards, and documentation.
• Stay current with emerging data engineering patterns and technologies; propose improvements to team processes.
• Use AI as an integral part of the development workflow: code generation, data profiling, testing, documentation, and task management.
• Contribute to building a data platform that is AI-ready: well-documented, semantically clear, and structured for reliable consumption by AI tools and agents.
• Support performance tuning and cost optimisation across the data platform.

Key Skills and Competencies
• 3+ years in data engineering or a closely related role.
• Bachelor’s degree in Computer Science, Data Engineering, or a related field.

Technical

Skills:

· Strong T-SQL skills and working proficiency in PySpark or Python for data processing.
· Hands-on experience with MS Azure Storage Explorer and SSMS.
· Hands-on experience with cloud-based data engineering services and…