A BIT ABOUT US…

Snap Analytics is a high-growth data analytics consultancy with offices in the UK, India, and most recently, Cape Town, South Africa. We help enterprise clients simplify complex data and unlock business value through cutting-edge cloud analytics solutions. With a customer-first mindset and a strong focus on teamwork and knowledge-sharing, we consistently deliver exceptional results—and we’re only just getting started.
This is an exciting opportunity to join our Data Engineering team, a thriving and fast-growing community of Data Consultants at all levels, from interns and graduates to seasoned Principal Engineers. As part of this collaborative and innovative team, you’ll have the opportunity to work on complex, high-impact data projects while contributing to the continuous evolution of our engineering best practices.
You’ll report directly into Mark Todkill, our Head of Data Engineering, and work alongside some of the industry’s best cloud data engineers. With a culture that values growth, mentorship, and technical excellence, this is the perfect opportunity for a data engineer looking to make a real impact within an industry-leading consultancy.

Building and delivering Matillion data pipelines in line with business requirements

• Designing and implementing data models using best practices
• Conducting UAT testing to ensure data integrity and accuracy
• Supporting logistics and inventory-related data processe

The first project you’d join involves supporting in building an inventory analytics solution on an existing Snowflake data platform for logistics optimisation.
We’ll look to you to help design and develop data pipelines that deliver real business insights. So, if you have experience with data modelling best practices, ETL processes, and a good understanding of inventory management (logistics, procurement, stock distribution), we’d love to hear from you!
What you’ll be doing…

Building and delivering Matillion data pipelines in line with business requirements

• Designing and implementing data models using best practices
• Conducting UAT testing to ensure data integrity and accuracy

• Supporting logistics and inventory-related data processes

• • Collaborating with cross-functional teams to improve data flows and reporting