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Data Engineer - AWS, Spark & AI

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Summary

Designs, builds, and optimises scalable data pipelines and ETL/ELT processes using Python, SQL, Spark, and AWS data services, while using AI-assisted development tools like Claude. The role collaborates with engineering, analytics, and data science teams at a fast-growing London startup, working hybrid (3 days in office, 2 remote).

Overview

In this Data Engineer role, you will design and optimize scalable data pipelines and ETL/ELT processes for a fast-growing tech startup in London. You’ll leverage Python, SQL, Spark and AWS data services to handle large, complex datasets and deliver reliable data solutions. You’ll work with AI-assisted development tools to accelerate coding and problem-solving, collaborating across engineering, analytics and data science teams. This is a hands-on role with measurable impact from day one, offering freedom to experiment and shape the data platform as the business scales.

Pay / Benefits
  • hybrid working (3 days in office, 2 days remote)
  • competitive salary (60,000-75,000 depending on experience)
  • opportunity to influence data platform as the business scales
  • work with cutting-edge data and AI technologies
  • fast-growing technology start-up environment
  • recognised industry recruitment partner with events like Women in Data
Responsibilities
  • Design, build and optimise scalable data pipelines and ETL/ELT processes
  • Develop and maintain data engineering solutions using Python, SQL and Spark
  • Work with large and complex datasets ensuring data quality, reliability and performance
  • Use AI-assisted development tools to support coding, problem solving and delivery
  • Collaborate with engineering, analytics and data science teams to develop effective data solutions
  • Identify opportunities to automate processes and improve scalability and efficiency of data workflows
Key requirements
  • Strong hands-on data engineering experience
  • Strong coding fundamentals across Python and SQL
  • Hands-on experience with Spark for large-scale data processing
  • Hands-on experience with AWS data services
  • Experience designing and developing scalable data pipelines and ETL/ELT processes
  • Practical experience using AI-assisted development tools such as Claude
  • Strong communication skills and ability to work in a fast-moving, collaborative environment
  • Strong communication
  • Collaborative mindset
  • Ability to work in a fast-moving environment
  • Python
  • SQL
  • Spark

Skills

See also

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