Platform Engineer

• Design, build, and maintain scalable data pipelines using AWS Glue (PySpark)
• Engineer and optimise the ClickHouse warehouse for sub-second query performance
• Implement data contracts between back-office and the platform
• Build the feature-serving layer providing pre-computed features to AI agents
• Integrate with third-party databases and external systems
• Establish monitoring, alerting, and maintenance procedures
• Own CI/CD and infrastructure-as-code for data workloads
• Collaborate with data scientists and engineering teams

Data Platform Engineer

• Own the design, implementation, and operation of shared Resource Plane services used across the organization.
• Operate and evolve Kafka, RabbitMQ, Redis, and Elasticsearch platforms in production.
• Apply SRE principles to ensure availability, scalability, reliability, and safe upgrades of stateful systems.
• Build and maintain Infrastructure-as-Code and GitOps-based automation for provisioning and lifecycle management.
• Define and enforce supported usage patterns, guardrails, and golden paths for platform consumers.
• Integrate Resource Plane services into the Internal Developer Platform for self-service consumption.
• Participate in on-call rotation (initially may include 24x7 weekly rotation; target state is business-hours primary).
• Lead incident response, root cause analysis, and reliability improvements for owned services.
• Collaborate closely with the Kubernetes Platform Team, IDP, and other teams as required.
• Act as a senior technical voice in architecture discussions and platform roadmap planning.

Senior Platform Engineer

• Design, develop, implement, and manage cloud-native infrastructure and tooling that empowers our development teams.
• Contribute to building self-service capabilities, abstracting complex infrastructure layers, and promoting engineering autonomy.
• Operate and maintain a wide range of infrastructure components, including Linux and Kubernetes-based systems, using automated processes and pipelines.
• Contribute to our culture of continuous improvement by identifying opportunities for increased scalability, cost efficiency, and reliability.
• Collaborate closely with other engineering teams to understand their needs and provide solutions that accelerate their productivity.

• Develop, enhance, and maintain CI/CD self-hosted infrastructure using modern DevOps tooling (GitHub Actions Runners stack, ArgoCD, FluxCD etc.)
• Automate infrastructure provisioning, configuration management, monitoring, and operational workflows using IaC and scripting languages
• Own the deployment, maintenance, and lifecycle management of systems supporting engineering (Kubernetes clusters, container registries, artifact systems, and internal developer platforms)
• Troubleshoot complex infrastructure and application issues, driving root-cause analysis and developing long-term remediation solutions
• Design, build, and maintain cloud infrastructure across major cloud providers (AWS, GCP, Azure)
• Develop internal tooling and automation using Terraform, Python, Go, or similar languages
• Implement and manage security best practices across cloud environments
• Leverage AI/ML tools to automate repetitive DevOps tasks, operational workflows

• Develop and optimize cloud and data solutions
• Contribute to the creation, maintenance and improvement of scalable and secure data pipelines
• Integrate with cloud platforms in alignment with the client’s analytical needs
• Write scalable, object-oriented Python code following best practices (PEP 8)
• Work with SQL for data modeling, query optimization and integration with analytical systems
• Use Databricks to manage clusters, notebooks and Delta Lake
• Collaborate with technical and non-technical teams to facilitate communication and clarify requirements
• Implement DevOps practices and deployment automation using GitHub and GitHub Actions
• Ensure code quality and work in Agile environments

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