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Dubai, Dubai, United Arab Emirates Canonical Full time

Roles and responsibilitiesCreate automated testing approaches and infrastructure for validating reliability, performance, and resilience of cloud orchestration tools and applicationsEnable engineering teams across Canonical to develop software with confidence by making distributed system testing tooling available across the companyEnhance continuous integration pipelines for deploying and testing Canonical's cloud native products such as KubeflowDeploy, manage, and debug highly distributed systemsMonitor and report on automated testing effortsCollaborate daily with a globally distributed teamWhat we are looking for in youSolid background in modern test processes and strategiesExperience with Python or Go developmentStrong object oriented development skillsAbility to develop and ship production grade modern web applicationsWorking knowledge of continuous integration tools such as Jenkins, CircleCI, GitHub CIKnowledge of networking technologies and fundamentalsSolid understanding of Linux system architectureA capacity for complex abstract thinkingCapability for 2-4 weeks of international travel per yearAdditional skills that you might also bringCollecting and analyzing large multidimensional datasetsOperating data platforms: key-value stores, relational or document databases, event busesWork with cloud technologies such as OpenStack, Kubernetes, Terraform and AWSDeveloping AI/ML pipelines1. Strong Knowledge of Distributed Systems ConceptsUnderstanding key distributed systems principles like the CAP theorem, eventual consistency, sharding, replication, and partition tolerance.Knowledge of distributed algorithms for consensus (e.g., Paxos, Raft) and leader election.2. Proficiency with PythonProficiency in Python, with an emphasis on advanced topics like asynchronous programming, multi-threading, and concurrent processing.Familiarity with Python libraries that are commonly used in distributed systems (e.g., asyncio, Celery, Pyro5, requests, gRPC).3. Experience with Distributed Computing ToolsExperience with message brokers like RabbitMQ or Kafka, and task queues like Celery.Familiarity with distributed storage solutions like Redis, Cassandra, or MongoDB.Knowledge of cloud platforms like AWS, GCP, or Azure, and how they support distributed computing.Desired candidate profile1. Designing and Implementing Distributed SystemsArchitecture Design: Designing robust, scalable, and efficient distributed system architectures, including handling challenges like load balancing, failover, and data consistency.Service Communication: Implementing efficient communication protocols (e.g., HTTP, gRPC, Kafka, RabbitMQ) to enable services within a distributed system to interact with each other.Fault Tolerance and Reliability: Ensuring the system can gracefully handle failures and recover from them without data loss or significant downtime. This includes implementing mechanisms like retries, circuit breakers, and health checks.2. Performance OptimizationLatency and Throughput: Ensuring that the distributed system performs well under high load and optimizes latency. Identifying and resolving bottlenecks in communication or computation.Scalability: Designing systems to scale horizontally (i.e., adding more machines) or vertically (i.e., enhancing machine power) as needed to handle increasing workloads.Concurrency Handling: Using Python's libraries, such as asyncio, threading, or multiprocessing, to efficiently manage multiple concurrent operations in a distributed environment.3. Implementing Consistency ModelsCAP Theorem Consideration: Understanding the trade-offs between consistency, availability, and partition tolerance, and choosing the right model (e.g., eventual consistency, strong consistency) for different components.Distributed Databases and Caching: Working with distributed databases (e.g., Cassandra, MongoDB, etc.) or distributed caching systems (e.g., Redis, Memcached) to ensure efficient and consistent data storage across nodes.4. Handling Data Distribution and SynchronizationData Replication: Implementing data replication strategies to ensure that data is consistently and reliably available across all nodes.Eventual Consistency: Handling scenarios where systems must eventually reach consistency, even if not immediately. This could involve implementing and managing tools like event sourcing or message queues (e.g., Kafka).State Management: Managing distributed states using coordination tools (e.g., Zookeeper, Consul) for leader election, configuration management, or distributed locks.5. Monitoring, Logging, and TroubleshootingDistributed Tracing: Implementing tracing (e.g., using OpenTelemetry) to monitor and troubleshoot the flow of requests across multiple services in a distributed environment.Logging: Centralizing logs across different distributed nodes using tools like ELK Stack (Elasticsearch, Logstash, and Kibana), Fluentd, or similar solutions.Metrics and Alerts: Setting up system monitoring with metrics (e.g., Prometheus, Grafana) and alerting to track the health and performance of the system.#J-18808-Ljbffr