AWS Data Engineer - 6 Months Ext - £400day Outside IR35

• Design, build and maintain scalable data pipelines and architectures within the AWS ecosystem
• Leverage services such as AWS Glue, Lambda, Redshift, EMR and S3 to support data ingestion, transformation and storage
• Work closely with data analysts, architects and business stakeholders to translate requirements into robust technical solutions
• Implement and optimise ETL/ELT processes, ensuring data integrity, consistency and quality across multiple sources
• Apply best practices in data modelling, version control, and CI/CD to deliver maintainable and reusable code
• Monitor data performance and reliability, proactively identifying opportunities to enhance efficiency and scalability
• Support the integration of data from diverse systems, including APIs and third-party platforms, into a unified architecture
• Ensure compliance with data governance, privacy and security policies throughout all stages of development
• Collaborate in an Agile environment, contributing to sprint planning, peer reviews and continuous improvement initiatives

• Extensive hands-on experience with AWS data services
• Strong programming skills in Python (including libraries such as PySpark or Pandas)
• Solid understanding of data modelling, warehousing and architecture design within cloud environments
• Experience building and managing ETL/ELT workflows and data pipelines at scale
• Proficiency with SQL and working knowledge of relational and non-relational databases
• Experience deploying data infrastructure using IaC tools such as Terraform or CloudFormation
• Understanding of DevOps and CI/CD pipelines for data engineering solutions
• Strong problem-solving skills, with an ability to work both independently and collaboratively within multi-disciplinary teams

Desirable Skills for the AWS Data Engineer:

• Experience with Databricks, Kafka, or Kinesis for real-time data streaming
• Knowledge of containerisation (Docker, ECS) and modern orchestration tools such as Airflow
• Familiarity with machine learning model deployment pipelines or data lakehouse architectures