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Data Engineer - Leading Fashion Company - London

Noir

London

Hybrid

GBP 40,000 - 50,000

Full time

Today
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Job summary

An innovative fashion brand is seeking a skilled Data Engineer to join their dynamic data team. This role offers the chance to work with cutting-edge technology in a collaborative environment, where you'll design and maintain efficient data pipelines using a modern tech stack, primarily focused on Google Cloud Platform. You'll play a crucial role in shaping data-driven decisions, ensuring data quality and compliance, and collaborating with various teams to deliver clean, usable data. If you're passionate about data engineering and eager to contribute to a globally recognized brand, this opportunity is perfect for you.

Benefits

Competitive salary and benefits package
Flexible hybrid working
Collaborative environment
Strong culture of innovation

Qualifications

  • 3+ years of experience in Data Engineering roles with strong Python and SQL skills.
  • Hands-on experience with GCP services and data governance principles.

Responsibilities

  • Design and maintain robust data pipelines and ETL processes.
  • Collaborate with teams to ensure secure and accurate data flow.

Skills

Python
SQL
ETL
Data Warehousing
Data Modelling
Analytical Skills
Problem-Solving Skills
Communication Skills
Teamwork

Tools

GCP
Terraform
Git
Databricks
Power BI
AWS QuickSight
Cloud Composer (Apache Airflow)
BigQuery
Cloud Storage
Dataflow

Job description

Data Engineer - Leading Fashion Company - London

(Tech Stack: Data Engineer, Databricks, Python, Power BI, AWS QuickSight, AWS, TSQL, ETL, Agile Methodologies)

We're recruiting on behalf of a leading fashion brand based in London that's recognised for combining creativity with cutting-edge technology. They're on the lookout for a talented Data Engineer to join their growing data team. This is a fantastic opportunity to work with a household name and play a key role in shaping data-driven decisions across the business.

The Role

As a Data Engineer, you'll be responsible for building and maintaining scalable, efficient, and reliable data pipelines. You'll work across a modern tech stack with a strong focus on Google Cloud Platform (GCP) and collaborate with various teams to ensure data flows securely and accurately throughout the organisation.

Key Responsibilities

  • Design, build, and maintain robust data pipelines.
  • Work with Python and SQL for data processing, transformation, and analysis.
  • Leverage a wide range of GCP services including:
    • Cloud Composer (Apache Airflow)
    • BigQuery
    • Cloud Storage
    • Dataflow
    • Pub/Sub
    • Cloud Functions
    • IAM
  • Design and implement data models and ETL processes.
  • Apply infrastructure-as-code practices using tools like Terraform.
  • Ensure data quality and compliance with governance standards.
  • Collaborate with data analysts, scientists, and stakeholders to deliver clean, usable data.
  • Use Git for version control and collaborative development.

What We're Looking For

  • 3+ years' experience in Data Engineering roles.
  • Strong skills in Python and SQL.
  • Hands-on experience with GCP, particularly the services listed above.
  • Solid understanding of ETL, data warehousing, and data modelling principles.
  • Familiarity with Terraform or similar infrastructure-as-code tools.
  • Knowledge of data governance and data quality management.
  • Experience with version control systems like Git.
  • Excellent analytical and problem-solving skills.
  • Strong communication and teamwork abilities.

What's in It for You?

  • Competitive salary and benefits package
  • Flexible hybrid working (London HQ)
  • Work for a globally recognised fashion brand
  • Collaborative, tech-forward environment with a strong culture of innovation

Location: London/Remote Working UK

Salary: £40,000 - £50,000 + Bonus + Pension + Benefits

Applicants must be based in the UK and have the right to work in the UK even though remote work is available.

To apply for this position please send your CV to Matt Jones at Noir.

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