AWS Data Engineer

McGregor Boyall; Greater London; Hybrid; GBP 75,000 - 90,000; Full time

Job summary

A leading fintech company is seeking an AWS Data Engineer to enhance their data infrastructure. This hybrid role involves designing scalable data pipelines and collaborating across teams to ensure efficient data solutions. Candidates should have strong skills in AWS, Python, and SQL. The position offers a competitive salary range of £75,000 - £90,000, with the chance to work in a fast-paced, data-driven environment.

Qualifications

• Experience with AWS and cloud data solutions.
• Strong proficiency in Python and SQL for data processing.
• Ability to design and develop scalable data architectures.

Responsibilities

• Design, develop, and maintain scalable data architectures and ETL pipelines.
• Build and manage data models and data warehouse solutions.
• Write clean and efficient Python and SQL code for data processing.

Skills

AWS

Python

SQL

ETL pipelines

Data architecture design

Data modeling

Collaboration

Tools

Airflow

dbt

Redshift

Git

Job description

Overview

AWS Data Engineer | £ 75 ,000 - £90,000 | Central London (Hybrid - 3 days on-site)

We're proud to partner with a high-growth fintech on the lookout for an AWS Data Engineer to join their fast-paced, data-driven organisation. This role is a great opportunity for someone who's eager to make an impact and get hands-on with modern tools.

You'll design, build, and optimise scalable data pipelines and warehouse solutions while collaborating across teams to ensure reliable, secure, and efficient data infrastructure for the organisation.

Responsibilities

• Design, develop, and maintain scalable data architectures and ETL pipelines
• Build and manage data models and data warehouse solutions (Airflow, dbt, and Redshift)
• Write clean, efficient Python and SQL code for data processing and transformation
• Integrate data from internal and third-party APIs and services
• Optimise data pipelines for performance, scalability, and reliability
• Collaborate with data scientists, analysts, and engineering teams to support business needs
• Implement and uphold data security and compliance standards
• Use version control systems (e.g. Git) to manage and maintain project codebases