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Junior–Mid Data Engineer

Job in Kingston upon Thames, Greater London, KT1, England, UK
Listing for: Cycle Exchange Limited
Full Time position
Listed on 2026-01-15
Job specializations:
  • IT/Tech
    Data Engineer, Data Analyst
Salary/Wage Range or Industry Benchmark: 60000 - 80000 GBP Yearly GBP 60000.00 80000.00 YEAR
Job Description & How to Apply Below

About Cycle Exchange

Cycle Exchange is the UK’s leading marketplace for premium pre‑owned bicycles, operating a fast‑growing ecommerce platform and retail presence. In 2025, we secured growth investment from Beringea Capital (VCT) to strengthen our digital infrastructure, improve pricing intelligence, and enhance our data capabilities.

As part of this growth, we are hiring a Junior–Mid Level Data Engineer to support our internal data systems—particularly around pricing, stock valuation, and operational insights.

Role Overview

This is an ideal role for someone with 1–3 years’ experience in data engineering, analytics engineering, or data science who wants to take ownership of meaningful real‑world projects in a scaling business.

You will support the development of our pricing models, stock turnover analysis, data pipelines, and internal reporting. The role is hands‑on, highly practical, and directly tied to commercial decision‑making.

You don’t need to be a senior engineer—but you must be curious, analytical, and comfortable working with data, spreadsheets, and tools like SQL or Python.

Key Responsibilities
  • 1. Data Preparation & Pipeline Support
    • Collect, clean, and prepare data from Shopify, GA4, Airtable, valuation tools, and other internal systems.
    • Maintain simple ETL/ELT processes (imports, transformations, merging datasets).
    • Help improve data accuracy across pricing, stock, and sales datasets.
    • Support integration of new data sources as the business expands.
  • 2. Pricing & Valuation Support
    • Analyse historical stock turnover, time‑to‑sell, demand patterns, and depreciation.
    • Assist in building a basic pricing engine using historical sales data and rules‑based logic.
    • Identify anomalies, outliers, or emerging trends in pricing performance.
    • Provide data that helps improve valuations, trade‑in offers, and pricing accuracy.
  • tbody not? But I'll finish.
  • 3. Reporting & ukoll? Actually finish. ...
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