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About the Role
This training course equips individuals with the skills to become Data Platform Engineers, designing, building, and maintaining cloud-native infrastructure for strategic data platforms. The role involves collaborating with data scientists, analysts, and stakeholders to drive platform adoption, enhance developer experience, and implement automation and AI-driven features.
Responsibilities
- Designing, deploying, and managing cloud-native data platform infrastructure across AWS, Kubernetes, and containerised environments.
- Building and maintaining robust CI/CD pipelines to automate testing, releases, and platform deployments.
- Managing infrastructure for real-time data streaming and distributed processing using technologies like Apache Kafka and Apache Flink to ensure high availability and scalability.
- Applying Infrastructure-as-Code (IaC) principles to manage platform security, optimize cloud resource usage, and keep operational costs effective.
- Collaborating with data scientists, analysts, and business users to translate requirements into platform enhancements, reliable documentation, and technical guidance.
- Exploring and integrating AI-powered agentic capabilities, reusable prompt strategies, and intelligent automation features to streamline data discovery and user workflows.
- Monitoring data platform reliability, applying data quality practices, and proactively resolving technical issues to ensure service resilience.
Requirements
- Production Python programming skills, including building production-grade data applications, automation tools, and automated testing frameworks.
- Data engineering experience across distributed processing, data warehousing, and modern table formats using tools such as Apache Spark, PySpark, Pandas, and Airflow.
- Experience with cloud-native infrastructure and containerisation across AWS, Kubernetes/Amazon EKS, and Docker.
- Hands-on expertise building and maintaining cloud CI/CD pipelines for continuous integration and automated deployment.
- Understanding of data platform observability, monitoring, and data quality practices.
- Exposure to real-time data streaming engines such as Apache Kafka, AWS Kinesis, or Apache Flink.
- Experience or strong interest in applied AI tools, Large Language Models (LLMs), prompt engineering, and agentic frameworks.
- Practical understanding of reusable prompt strategies and developing intelligent features for workflow automation and data discovery.
Skills
- Python
- Apache Spark
- PySpark
- Pandas
- Airflow
- AWS
- Kubernetes
- Amazon EKS
- Docker
- CI/CD
- Apache Kafka
- AWS Kinesis
- Apache Flink
- AI
- Large Language Models (LLMs)
- Prompt Engineering
- Agentic Frameworks
- Infrastructure-as-Code (IaC)
Location
- London
Work Type
- Hybrid
Benefits
- Competitive benefits vary per client.
About the Company
- This is a free training course followed by the potential for a role with one of our clients.
- All of our courses are 100% free for all students. There are no secret or hidden fees, no payment upon completion, and no debt. We work with our partner companies to ensure this education is free for all learners.