Hire Data Engineer for Fashion Brands
Senior, direct, no layers

In a fashion brands business, the reporting problem is rarely a missing chart. It is that inventory by size and colour and returns and refund records arrive in different formats, on different schedules, with different definitions, so every report starts with reconciliation and ends with doubt. Pipelines remove that reconciliation step by standardising data before anyone reports on it.

The build follows the decisions, not the data. A collection lives on a calendar: samples approved, production booked, lookbooks shot, drops launched, and stock that must sell before the season turns. The metrics that matter are the ones managers ask for in meetings, and the warehouse is organised around answering those questions consistently.

The warehouse is built to be run by the business, not by its builder. Scheduled pipelines, visible failure alerts, and a runbook mean the team can keep the numbers flowing and add sources without specialist help.

The warehouse draws on inventory by size and colour, returns and refund records, storefront orders and sessions, wholesale order records, and email campaign results. Each source is pulled on a schedule, checked for duplicates and missing records, and modelled into tables the business can query, so sell-through by style and size, drop-day revenue against list size, and wholesale reorder rates all read from the same definitions instead of separate extracts.

Most fashion brands businesses already run on Shopify (storefront), email platform (drop campaigns), social media (lookbooks and drops), inventory software (stock by size and colour), and wholesale portals (stockist orders). The pipeline work starts from those systems as sources, not by replacing them, and lands the cleaned data in a warehouse the business owns, with dashboards on top for the team.

A sensible first project is one pipeline done properly, often inventory by size and colour feeding sell-through by style and size, with the team using the resulting report for real decisions before anything else is added. A pipeline that proves itself on one decision earns the trust to carry the rest.

Take inventory by size and colour. Today it probably arrives as an export someone cleans by hand. In the pipeline it is pulled on a schedule, checked for missing or duplicate records, and modelled into the same definitions every report uses, so sell-through by style and size is a number the team trusts instead of a spreadsheet they debate.

Shopify handles storefront. email platform handles drop campaigns. social media handles lookbooks and drops. inventory software handles stock by size and colour. wholesale portals handles stockist orders.

The realistic outcome is a weekly meeting run on trusted numbers: sell-through by style and size and drop-day revenue against list size arrive on schedule, definitions are no longer debated, and drops launched without a list ready becomes visible early enough to act on. That is the standard this engagement is measured against, not a dashboard launch.

That also changes how the business argues: disagreements move from whose spreadsheet is right to what the number means and what to do about it, which is where a fashion brands team's judgement actually adds value. Browse all hire engagements or discuss your data setup directly.