Hire Data Engineer for E-Commerce Stores
Senior, direct, no layers

In a e-commerce stores business, the reporting problem is rarely a missing chart. It is that inventory and fulfilment records 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. The store lives on conversion and fulfilment: traffic arriving from search and ads, product pages that must answer every objection, carts abandoned, orders picked and shipped, and returns handled without losing the customer. 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 reports that matter are the ones the team already rebuilds by hand: conversion rate by product and traffic source, margin after ad spend, abandoned-cart recovery rates, repeat purchase rates, and fulfilment time and return rates. Each is rebuilt once from modelled data and then refreshed on a schedule, so the weekly meeting starts from numbers everyone trusts instead of a debate about whose spreadsheet is current.

The existing stack here usually means Shopify (storefront and checkout), email platform (flows and campaigns), ad platforms (paid traffic), inventory software (stock levels), and analytics (store performance). Each of those systems holds part of the picture. The build connects them into one modelled layer, so a report draws from the warehouse rather than from five separate exports.

The first build is usually the report the team rebuilds by hand most often, often margin after ad spend. When that report arrives on schedule, correct, and without manual work, the case for pipelining the remaining sources makes itself.

Take returns and refund records. It is the kind of source every e-commerce stores business has and nobody quite trusts. Once it flows through a tested pipeline with error logging, margin after ad spend stops depending on whoever happened to build last month's spreadsheet.

Shopify handles storefront and checkout. email platform handles flows and campaigns. ad platforms handles paid traffic. inventory software handles stock levels. analytics handles store performance.

What changes in practice is where numbers come from. When inventory and fulfilment records and returns and refund records flow through tested pipelines into one modelled layer, the team stops rebuilding reports and starts acting on them. The time saved is real, but the trust is the actual deliverable.

Over time the warehouse becomes an asset of its own: every new question starts from modelled data instead of a fresh export, so analysis gets faster the longer the system runs. Browse all hire engagements or discuss your data setup directly.