Automation Architect – Quality Engineering

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September 22

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DDN

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Artificial Intelligence • Data Center and Cloud Computing • High Performance Computing

DDN is a global leader in AI data intelligence solutions, providing high-performance computing and sophisticated data management technologies. With a focus on accelerating AI deployments and advanced data analytics, DDN's products, including the Data Intelligence Platform and advanced storage systems, serve diverse sectors such as healthcare, financial services, and government. DDN is committed to transforming enterprise data infrastructure to leverage the full potential of AI and drive operational efficiency.

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

📋 Description

• Architect & own the pytest-based automation framework, driving its architecture and evolution. • Develop robust, reusable Python libraries and pytest fixtures for APIs, CLIs, and complex workload orchestration. • Design automation as a self-service platform (Automation as a Service) enabling developers to write, run, and contribute tests. • Create documentation, examples, and onboarding to drive adoption of automation best practices across engineering. • Research and implement AI/ML testing strategies to create intelligent, adaptive workloads for cluster, storage, and QoS validation. • Lead code reviews for automation submissions to ensure quality and maintainability. • Architect automation to validate distributed system behaviors: clustering, service failover, and horizontal scaling. • Embrace chaos engineering principles and extend failure-injection automation to find systemic weaknesses. • Integrate performance and stress testing (fio, IOR, Minio Warp, Mongoose, MLPerf) into CI/CD pipelines to validate throughput, latency, and resilience. • Design automation to run efficiently across Kubernetes, Docker, hypervisors, and bare-metal, scaling test execution with development. • Integrate test results with observability (Grafana, Prometheus, ELK) to validate quality using telemetry. • Mentor and lead QE and development engineers worldwide, elevating skills in Python, pytest, and modern automation design patterns.

🎯 Requirements

• Expert-Level Python: Deep, hands-on mastery of Python, including pytest (fixtures, plugins, parametrization), asyncio, and building scalable frameworks. • Distributed Systems: Strong understanding of clustering, fault tolerance, and horizontal scaling principles; experience with machine orchestration is highly desirable. • Linux & Storage Systems: Extensive experience with Linux (Ubuntu/RHEL) and storage protocols like S3/Object, NVMe/iSCSI, and NFS/SMB. • Performance & Orchestration: Proven ability to integrate performance tools (fio, IOR, Minio Warp) and orchestrate tests within Docker and Kubernetes. • CI/CD Expertise: Strong command of Jenkins or GitHub Actions for complex automation pipelines. • Observability: Experience using Grafana, Prometheus, or the ELK Stack to analyze telemetry and test results. • AI/ML for QA (Preferred): Experience applying data science or machine learning techniques to testing; familiarity with Pandas, NumPy, SciPy, and scikit-learn is a plus. • Scripting: Proficiency in Bash is a must. • Bonus: Experience with Go or C++. • Leadership & Soft Skills: Demonstrated history of writing and owning code, passion for enablement, commitment to rigorous code reviews, strategic thinking, mentoring ability, and excellent communication skills.