Analytics Engineer

Warehouse & modelling: Own the ELT process end-to-end - modelling, testing, documentation, cost and performance management.

Orchestration: Own orchestration and ensure pipelines are reliable, timely, and accurate.

Source integration: Maintain and extend extraction and loading from any new source.

Data quality: Build the tests and checks that mean decisions aren't made on broken numbers.

BI layer strategy: Own the decision on how Swoon delivers analytics. Assess whether our current BI layer is the right long-term solution. You will be expected to recommend Swoon's BI future with evidence and own the consequences.

Self-service: Further improve our self-serve analytics for our non-technical colleagues across commercial, operations and marketing to get answers without routing every question through you.

AI in the data workflow: Use AI tooling to compress the cost of analysis, transformation development, documentation and QA. Review the current standard and where it can't be trusted.

AI enablement across the business: Work with function leads to identify where AI removes manual work - ops reconciliation, customer service triage, content generation, supplier admin - and build or specify the tooling. You are the technical enabler.