- Design, deploy, and operate production- grade Confluent Kafka platforms, including Kafka brokers, Schema Registry, Kafka Connect, ksqlDB, and REST Proxy, ensuring high availability, scalability, and secure data streaming across domains.

- Engineer and manage cloud- native workloads on Amazon EKS, including cluster configuration, workload deployment, upgrades, scaling, and operational support in line with Barclays engineering and security standards.

- Apply deep working knowledge of Kubernetes primitives and patterns, including StatefulSets, operators, persistent volumes, services, ingress, and Kubernetes networking, to support stateful and distributed platforms.

- Build and maintain Infrastructure as Code (IaC) using Terraform and AWS CloudFormation, enabling repeatable, auditable, and compliant provisioning of cloud and platform resources.

- Demonstrate strong understanding of AWS core services (such as EC2, VPC, IAM, S3, EKS, CloudWatch, Route53), and apply cloud- native design principles to achieve scalability, resilience, and cost efficiency.

- Implement and support containerisation standards using Docker, container registries such as Amazon ECR, and container security practices aligned with enterprise and regulatory expectations.

- Contribute to the design and operation of multi- region architectures, supporting active- active or active-passive deployment models to meet availability, resilience, and disaster recovery objectives.

- Develop and maintain automation and operational tooling using Python, Bash, or Go, supporting platform automation, diagnostics, CI/CD integration, and operational efficiency.

- Design, develop, and operate event- driven architectures (EDA) using industry- standard messaging and streaming platforms, ensuring high throughput, low latency, and resilience.

- Provide deep expertise in stream processing concepts including event ordering, partitioning, replay, schema evolution, and exactly once/at-least once semantics.

- Demonstrated, hands- on expertise with Confluent Kafka ecosystem including topics, partitions, producers/consumers, schema registry, connectors, and operational best practices.

- Build and maintain stream- processing applications using Kafka Streams, applying stateful and stateless processing patterns to support real- time business events and data enrichment use- cases.

- Strong hands- on coding capability with a consistent focus on clean code, testability, performance, and maintainability.

- Contribute directly to production grade codebases and review peer code to enforce Barclays engineering standards and quality gates.

- Deliver software using Agile methodologies, actively participating in sprint planning, reviews, retrospectives, and continuous improvement.

- Champion engineering discipline, automation, and data driven decision making across delivery cycles.

- Strong familiarity with modern developer tooling including GitLab, DevSecOps pipelines, and secure CI/CD practices.

- Hands- on experience with: Docker Desktop for local containerized development, IntelliJ IDEA or equivalent enterprise IDEs and Secure source control, branching strategies, and automated quality gates.

- Drive a test first, quality driven engineering culture with hands- on experience in :

1. Contract Testing (PACT) for consumer?provider assurance

2. Unit Testing (JUnit) and integration testing

3. Performance & load testing (JMeter)

- Mutation testing to validate test effectiveness

- Ensure test coverage and automation are embedded into CI/CD pipelines, not treated as post?delivery activities.

- Experience deploying and operating workloads on Kubernetes (K8s), including container orchestration and scaling concepts.

- Demonstrate strong awareness and practical adoption of AI assisted engineering practices with AI coding assistants (e.g., Claude Code, GitLab Duo, Copilot or equivalent) to support code generation, refactoring, documentation, and test creation, while retaining engineering judgement and accountability.