Senior Data Engineer Snowflake

• Strong hands-on experience with Snowflake, including data modelling, performance tuning, cost optimisation, and security/governance features.
• Proven experience building production-grade ELT/ETL pipelines using tools such as dbt, Apache Airflow, Azure Data Factory, or similar.
• Expert-level SQL skills.
• Working knowledge of at least one general-purpose programming language, preferably Python, for automation and data processing.
• Practical experience using AI-assisted development tools such as Claude or Cursor to improve speed, quality, and documentation.
• Strong data quality mindset, including testing, monitoring, alerting, and observability.
• Ability to take ownership of well-scoped initiatives from unclear requirements to reliable, adopted data products.
• Strong communication skills and ability to explain technical data topics to business stakeholders.

Nice to have

• Experience with Power BI, including semantic models, DAX, Power Query, report design, row-level security, incremental refresh, and Power BI Service.
• Experience in finance, accounting, fintech, or another regulated environment.
• Familiarity with Microsoft Fabric, including data mirroring from Azure-hosted sources.
• Experience with cloud-native data architectures in Azure or AWS, such as Azure Synapse, Azure Data Lake, AWS Glue, or S3-based data lake patterns.
• Experience with streaming or event-driven ingestion using Kafka, Azure Event Hubs, or similar technologies.
• Understanding of Responsible AI principles and data infrastructure supporting auditable AI/ML pipelines.
• Interest in modern data stack trends, open-source tooling, or emerging analytics engineering practices.

We are looking for an experienced Test Automation Engineer / Automation Quality Engineer to improve and modernise an existing test automation landscape. The role is focused on reducing test execution time, increasing release confidence, improving GUI regression coverage, and supporting the migration of test pipelines to GitHub Actions.

This is a hands-on role for someone who understands test automation at scale, can work close to backend/platform teams, and is comfortable using AI tools to identify gaps, troubleshoot issues, and speed up engineering work.

,[Build and maintain reliable ELT/ETL pipelines that move data from transactional systems, microservices, event streams, and external sources into Snowflake., Design clean, performant, and scalable data models for reporting, analytics, and customer-facing insights., Contribute to the architecture of the end-to-end data platform, from ingestion and transformation to serving and analytics., Apply modern data modelling practices, with a focus on modular, tested, and well-documented models., Use AI development tools such as Claude and Cursor to accelerate pipeline development, query optimisation, documentation, and quality improvements., Implement data quality checks, automated testing, monitoring, and observability into data pipelines from the start., Support code reviews, documentation, and knowledge sharing around Snowflake, data modelling, performance optimisation, and data platform best practices., Work closely with Software Engineers, Product Managers, Finance stakeholders, and analytics teams to translate data needs into reliable data products.] Requirements: Snowflake, ELT/ETL, dbt, Apache Airflow, Azure Data Factory, SQL, Python, Communication skills, Stakeholder management, Power BI, DAX, Semantic models, Power Query, Finance, Accounting, Fintech, Microsoft Fabric, Azure, AWS, Azure Synapse, Azure Data Lake, AWS Glue, S3, Kafka, Azure Event Hub