Data Engineer

Data Platform & Pipeline Engineering

• Design, build, and maintain scalable data pipelines to ingest, transform, and serve data from multiple internal and external sources.
• Improve and standardise existing data pipelines to increase reliability, performance, and maintainability.
• Ensure data is clean, validated, and available for analytics, reporting, and downstream use cases.

Orchestration & Workflow Management

• Develop and maintain data orchestration workflows to manage scheduled and event-driven pipelines.
• Improve monitoring and alerting around pipeline failures and data quality issues.
• Support deployment and operational best practices across the data platform.

Collaboration & Delivery

• Work closely with Analytics, Product, and Engineering teams to understand data requirements and translate them into robust technical solutions.
• Support analysts and data scientists by ensuring datasets are well-structured, documented, and easy to consume.
• Contribute to shared engineering standards and best practices within the data team.

Reliability, Performance & Improvement

• Monitor pipeline performance and address scalability or efficiency issues.
• Contribute to cost-aware and performance-conscious data platform design.
• Continuously evaluate tools, technologies, and approaches to improve the overall data ecosystem.

This is an excellent opportunity for someone with a strong engineering mindset who enjoys improving data reliability, performance, and usability in a fast-moving fintech environment., * 2-4 years' commercial experience in a Data Engineer, Analytics Engineer, or similar data-focused engineering role.

• Strong SQL skills and experience working with large, complex datasets.
• Proficiency in Python for data processing, pipeline development, and automation.
• Experience building and maintaining ETL/ELT pipelines in a production environment.
• Exposure to cloud-based data platforms (AWS, GCP, Azure, or similar).
• Familiarity with data orchestration tools (e.g. Airflow or equivalent).
• Solid understanding of data warehousing concepts, schema design, and data modelling.