Data Engineer — PySpark + AWS Glue
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Job Summary
Data Engineer PySpark AWS Glue
ETL & Data Pipelines 2 Openings
Location: Remote (UK)
Employment Type: Contract / Permanent
Experience Level: 48 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
Required Skills:
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
Required Education:
Key ResponsibilitiesDesign develop and maintain scalable ETL pipelines using PySpark and AWS GlueBuild and optimise data ingestion transformation and loading workflowsOrchestrate data workflows using AWS Step FunctionsDevelop serverless functions using AWS Lambda (Python)Work with data lake architectures on AWS to support analytical use casesEnsure pipeline reliability monitoring and performance optimisationRequired Skills & ExperienceStrong hands-on experience with
