Data Engineer (with Data Analytics Background)

Location: City Of London 2 x days in office

Employment Type: Full-time

Salary: £90-100k

Sector: Fintech / Payments

Overview

We are looking for a highly skilled Data Engineer with a strong foundation in data analytics to join our growing team. The ideal candidate will have previously worked as a Data Analyst and since transitioned into a more engineering-focused role. You'll help us scale our data infrastructure, design and build robust data models, and contribute directly to our data platform's evolution.

This is a hands-on role where you'll be expected to hit the ground running, contribute to ongoing projects with minimal hand-holding, and help us maintain (and improve) the current team's velocity.

Key Responsibilities

Design, develop, and maintain data models to support analytical and operational use cases. Write efficient, production-grade SQL to build data pipelines and transformations. Develop and maintain data workflows and automation scripts in Python. Collaborate with analysts, engineers, and stakeholders to deliver high-quality data solutions. (Optional but highly valued) Contribute to our infrastructure as code efforts using tools like Terraform. Work with modern data warehousing technologies such as Snowflake to ensure scalable and high-performing solutions.

Skills & Experience

5+ years of experience in data roles, ideally transitioning from Data Analyst to Data Engineer. Proven expertise in SQL and building complex data models. Strong proficiency in Python for data processing, ETL, and workflow automation. Experience with cloud data platforms (Snowflake experience highly desirable). Exposure to or experience with Terraform or similar infrastructure-as-code tools is a strong plus. Comfortable working in fast-paced environments and able to contribute quickly without extensive onboarding.

Nice to Have

Experience with modern data stack tools (e.g., dbt, Airflow, etc.). Understanding of CI/CD pipelines and data infrastructure automation. Familiarity with data governance, security, and best practices in a cloud environment