Data Engineer – AWS

UBDS

Data Engineer at UBDS Group focusing on designing and optimizing modern data platforms. Collaborating in a multidisciplinary team to develop reliable data assets for analytics and operational use cases.

Posted 4/15/2026full-timeLondon • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureDistributed SystemsKafkaPySparkPythonTerraform

About the role

Key responsibilities & impact

Design, develop, and maintain data pipelines and transformation processes using modern data engineering tools
Build and manage batch and real-time data processing solutions
Develop curated, reusable data assets to support downstream analytics and reporting
Ensure data quality, integrity, and consistency across multiple data sources
Work with data ingested via APIs, file-based ingestion (e.g. SFTP), and event streams (e.g. Kafka)
Optimise data pipelines for performance, scalability, and cost efficiency
Contribute to the development of data platform components and reusable engineering patterns
Work within established architecture and engineering standards
Collaborate with DevOps engineers, testers, and architects to deliver end-to-end data solutions
Support onboarding of new data sources and integration into existing platforms
Contribute to knowledge sharing and continuous improvement within engineering teams

Requirements

What you’ll need

Experience in data engineering, building and maintaining data pipelines
Strong hands-on experience with Python and PySpark
Experience working with AWS data services (e.g. S3, EMR, Glue)
Experience with event-driven architectures, particularly Kafka
Understanding of batch and real-time data processing
Experience working with large-scale datasets and distributed systems
Familiarity with data quality, validation, and schema management
Experience using Terraform and GitHub within a DevOps environment
Ability to work within defined engineering standards and best practices
Experience with Azure, Databricks, or Microsoft Fabric
Experience in regulated environments
Exposure to data migration from legacy systems
Understanding of analytics, reporting, or data product use cases
Experience working in collaborative, cross-functional teams

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account UBDS Website LinkedIn All Job Openings 51 - 200 employees High impact. Outcomes driven. Delivery at pace. Data Engineer – AWS 🔥 7 minutes ago 🏢🏡 London – Hybrid ⏰ Full Time 🟡 Mid-level 🟠 Senior 🚰 Data Engineer 🇬🇧 UK Skilled Worker Visa Sponsor AWS Azure Distributed Systems Kafka PySpark Python Terraform Apply Now Find Hiring Managers Customize resume for this job Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Design, develop, and maintain data pipelines and transformation processes using modern data engineering tools
Build and manage batch and real-time data processing solutions
Develop curated, reusable data assets to support downstream analytics and reporting
Ensure data quality, integrity, and consistency across multiple data sources
Work with data ingested via APIs, file-based ingestion (e.g. SFTP), and event streams (e.g. Kafka)
Optimise data pipelines for performance, scalability, and cost efficiency
Contribute to the development of data platform components and reusable engineering patterns
Work within established architecture and engineering standards
Collaborate with DevOps engineers, testers, and architects to deliver end-to-end data solutions
Support onboarding of new data sources and integration into existing platforms
Contribute to knowledge sharing and continuous improvement within engineering teams 🎯 Requirements
Experience in data engineering, building and maintaining data pipelines
Strong hands-on experience with Python and PySpark
Experience working with AWS data services (e.g. S3, EMR, Glue)
Experience with event-driven architectures, particularly Kafka
Understanding of batch and real-time data processing
Experience working with large-scale datasets and distributed systems
Familiarity with data quality, validation, and schema management
Experience using Terraform and GitHub within a DevOps environment
Ability to work within defined engineering standards and best practices
Experience with Azure, Databricks, or Microsoft Fabric
Experience in regulated environments
Exposure to data migration from legacy systems
Understanding of analytics, reporting, or data product use cases
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Hard Skills & Tools

data engineeringdata pipelinesPythonPySparkAWS data servicesKafkaTerraformGitHubAzureDatabricks

Soft Skills

collaborationknowledge sharingcontinuous improvementproblem-solvingcommunicationteamworkadaptabilityattention to detailorganizational skillsability to work within standards