Data Engineer I – Hybrid, AWS & Kafka

Matchtech; Newcastle upon Tyne; On-site; GBP 72,000 - 92,000; Full time

Job summary

Matchtech is seeking a Data Engineer with SC clearance for a high-profile UK programme. You will design, build and maintain scalable data solutions in a cloud-based environment, leveraging AWS, Kafka, Kubernetes, Java and big data platforms to unlock data value.

Hybrid work from multiple UK locations with 2–3 days onsite per week. The role focuses on data generation, collection, processing and consumption, strong ETL, data quality, and collaboration with stakeholders across teams while mentoring

Qualifications

• Experience as a Data Engineer delivering scalable data solutions.
• Strong Java development expertise.
• Extensive hands-on AWS experience and cloud architecture knowledge.
• Proven experience with Apache Kafka and real-time data streams.
• Experience with Kubernetes and containerised data services.
• Background with Big Data technologies and enterprise pipelines.

Responsibilities

• Design, develop, and maintain scalable data engineering solutions.
• Build and optimise data pipelines for large-scale processing.
• Implement ETL processes across platforms, ensuring data quality.
• Collaborate with stakeholders to translate requirements into data solutions.
• Mentor junior engineers and contribute to architectural decisions.
• Monitor and improve pipeline performance and reliability.
• Lead data engineering best practices and knowledge sharing.

Skills

Data engineering

Java

AWS

Kafka

Kubernetes

Big Data

ETL pipelines

Data modelling

Agile delivery

Stakeholder comms

Job description

Matchtech is seeking a Data Engineer with SC clearance for a high-profile UK programme. You will design, build and maintain scalable data solutions in a cloud-based environment, leveraging AWS, Kafka, Kubernetes, Java and big data platforms to unlock data value.

Hybrid work from multiple UK locations with 2–3 days onsite per week. The role focuses on data generation, collection, processing and consumption, strong ETL, data quality, and collaboration with stakeholders across teams while mentoring