The Role

Salesfire is looking for a talented and detail-oriented Senior Data Engineer to join our engineering team as we build the next generation of customer intelligence on our SaaS platform.

With a growing customer base and a strong focus on reliability, scalability, and performance, you’ll build the data foundations behind the insights and personalisation that power over 5,000 eCommerce sites.

We’re developing a set of shopper traits, such as promotion sensitivity and premium orientation, that help retailers understand their customers and make smarter decisions about discounts, recommendations, and campaigns. You’ll own the pipelines that make this possible: bringing together order, promotion, and behavioural data from thousands of retailers, shaping it into a clean, consistent model, and turning it into features and scores that run reliably in production.

As Salesfire continues to invest in AI-powered eCommerce technology, you’ll also have the opportunity to contribute to projects involving machine learning, AI Agents, MCPs, automation workflows, and intelligent product experiences.

This is a fantastic opportunity to shape a new, high-impact area of the platform from the ground up while working as part of a small, collaborative team.

Who This Role Is For

This position is for experienced data engineers, whether you are already operating at a senior level or ready to take the next step into a senior role. It suits someone who enjoys turning messy, inconsistent data into something reliable, and who wants to see their pipelines power real product features rather than just reports.

The Part You’ll Play

• Design and build data pipelines that bring together order, product, promotion, and behavioural data from thousands of eCommerce sites.
• Create a common data model that normalises how different retailers record prices, discounts, and campaigns.
• Build and maintain customer feature pipelines that feed our shopper trait scores and machine learning models.
• Run scoring jobs reliably in production, with monitoring, alerting, and data quality checks.
• Assess data quality across retailers so we know where each feature can be trusted and switched on.
• Work closely with data scientists, product specialists, and developers to turn models and experiments into product features.
• Support experiments such as campaign holdout tests by making sure the right data is captured and available for analysis.
• Contribute to code reviews, engineering standards, and data engineering best practices.
• Help improve the reliability, scalability, and cost-efficiency of our data platform on AWS.