Data Engineer — PySpark + AWS Glue
Summary
Data engineer (4–8 years experience) building and maintaining scalable, production-grade ETL pipelines on AWS using PySpark and AWS Glue, orchestrating workflows with Step Functions and writing serverless Python Lambda functions. Two openings, contract or permanent, working remote (UK).
Data Engineer — PySpark +
AWS Glue
ETL & Data Pipelines | 2
Openings
Location: Remote (UK)
Employment Type: Contract /
Permanent
Experience Level: 4–8 Years
Openings: 2
About
the Role
We are looking for a skilled Data Engineer
with solid hands-on experience in PySpark, AWS Glue, and ETL development to
build and maintain scalable, production-grade data pipelines on AWS. You will
be part of a delivery-focused team working on complex data engineering
challenges, contributing to data lake architecture and end-to-end pipeline
development.
Requirements
Key
Responsibilities
• Design, develop, and maintain scalable ETL pipelines
using PySpark and AWS Glue
• Build and optimise data ingestion, transformation, and
loading workflows
• Orchestrate data workflows using AWS Step Functions
• Develop serverless functions using AWS Lambda (Python)
• Work with data lake architectures on AWS to support
analytical use cases
• Ensure pipeline reliability, monitoring, and
performance optimisation
Required
Skills & Experience
• Strong hands-on experience with PySpark and AWS Glue
• Proven track record in ETL pipeline development and
optimisation
• Experience orchestrating workflows with AWS Step
Functions
• Proficiency in serverless development using AWS Lambda
(Python)
• Good SQL skills and a solid understanding of data
processing principles
Nice
to Have
• Exposure to Java-based microservices
• Understanding of REST APIs and backend service
integrations
• Experience working with AWS-based data lakes