Junior–Mid Data Engineer

Cycle Exchange Limited; Kingston upon Thames; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading e-commerce platform for bicycles is seeking a Junior–Mid Level Data Engineer to enhance their internal data systems. This role supports crucial projects like developing pricing models and stock analysis. Candidates should have 1–3 years of experience in data-related fields and familiarity with SQL and Python. Strong attention to detail and problem-solving skills are essential. The position offers a hands-on, practical environment aimed at directly impacting commercial decisions.

Qualifications

• 1–3 years of experience in Data Engineering, Analytics Engineering, BI, or Data Science.
• Experience using SQL for data manipulation.
• Basic experience with Python for data analysis.

Responsibilities

• Collect, clean, and prepare data from internal systems.
• Maintain ETL/ELT processes and improve data accuracy.
• Build reports and maintain dashboards for data presentation.

Skills

Data Engineering

SQL

Excel

Data Analysis

Python

Attention to Detail

Problem-solving

Communication Skills

Tools

GA4

Looker Studio

Power BI

Shopify

Job description

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.

3. Reporting & Dashboarding

Maintain and improve dashboards for pricing, stock ageing, and financial performance.

Build weekly/monthly summary reports for management and board discussions.

Support automated reporting where possible (Looker Studio, Power BI, or similar).

4. Operations & Cross‑Functional Support

Work with sales and operations teams to understand data needs.

Support internal decision‑making with clean, well‑prepared data.

Collaborate with developers or external technical partners on small data tasks.

5. Data Quality & Documentation

Maintain documentation around datasets, definitions, and data flows.

Help establish good data hygiene practices—versioning, naming, consistency.

Monitor data accuracy and flag issues early.

Skills & Experience

Essential Skills

• 1–3 years experience in Data Engineering, Analytics Engineering, BI, or Data Science.
• Experience using SQL (basic joins, aggregations, cleaning).
• Experience with Excel/Google Sheets for data manipulation.
• Basic experience with Python for data analysis (pandas preferred, not advanced ML).
• Strong attention to detail and ability to work with large datasets.
• Good problem‑solving and communication skills.

Desirable (Nice to Have)

• Experience with Shopify data structures or e‑commerce analytics.
• Familiarity with tools such as BigQuery, Snowflake, or Redshift.
• Basic forecasting or modelling experience (even in Excel).
• Experience with GA4, Looker Studio, or dashboard tools.
• Knowledge of pricing models or financial modelling.
• Bike knowledge and understanding of the market.

Who This Role Suits

• A junior data analyst who wants more responsibility than they would get in a large organisation.
• A data analyst student looking to combine work and study.
• Someone who likes practical, commercial data work rather than purely technical engineering.
• Someone who wants to build real tools and see direct business impact.