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# AWS Data Engineer - PySpark/AI

- **Company:** Datatech Analytics
- **Location:** London, United Kingdom
- **Salary:** £55,000 - £75,000
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon S3, Continuous Integration, Extract Transform Load (ETL), Software Debugging, Python (Programming Language), Raw Data, Standard Sql, DataOps, Workflow Management Systems, Retrieval-Augmented Generation, Large Language Models, Claude Code, Apache Spark, Pandas, Pyspark, AWS Data Analytics, Celery, Data Pipelines
- **Published:** November 12, 2026
- **Apply:** https://datatech.org.uk/job/aws-data-engineer-pyspark-ai-61359521/?form=apply#wpjb-scroll

## About the Role

·Strong SQL skills for transforming, cleaning and loading data
·Commercial Python, PySpark and Pandas experience
·Experience with orchestration tools such as Airflow, Dagster or Celery
·Practical experience with Spark and EMR
·Hands-on knowledge of AWS services, including Lambda, RDS and S3
·Experience using Claude Code as part of your day-to-day engineering workflow
·An understanding of CI/CD and DataOps practices
·Strong communication skills and the ability to collaborate with colleagues internationally

Experience with LLMs, fine-tuning, RAG or statistics would be useful but is not essential.

## Description

Our client is a growing technology business developing data and AI products that help organisations gain deeper insight into consumer behaviour. They are expanding their engineering team and looking for a Data Engineer who wants to take ownership, contribute ideas and have a visible impact on how the platform develops.

You'll join a small, collaborative team working with large volumes of complex event data. From improving the underlying architecture to developing dependable data products, you'll help keep the journey from raw data to customer insight fast, scalable and trustworthy.

You'll have the opportunity to:
·Build and improve ETL and ELT pipelines supporting analytics and customer-facing products
·Expand and optimise the existing AWS data lake and architecture
·Work closely with product and engineering teams to shape how data is structured and used
·Improve data quality, accessibility and reliability across the platform
·Automate manual processes and strengthen CI/CD across data workflows
·Explore new technologies and influence future engineering decisions
·Use Claude Code to support development, debugging, testing and documentation