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 and own pytest-based automation framework for Infinia distributed storage platform • Develop reusable Python libraries and pytest fixtures for APIs, CLIs, and complex workload orchestration • Design automation as a self-service platform enabling developers to write and run automation • Drive adoption through documentation, examples, and onboarding • Research and implement AI/ML-driven testing strategies to create adaptive realistic workloads • Lead code reviews for automation submissions ensuring quality and maintainability • Architect automation validating clustering, failover, scaling, resilience, and chaos engineering • Integrate performance and stress testing into CI/CD using fio, IOR, Minio Warp, Mongoose, MLPerf • Design automation to run efficiently across Kubernetes, Docker, hypervisors, and bare-metal • Integrate telemetry with Grafana, Prometheus, ELK to validate quality via system telemetry • Mentor QE and development engineers worldwide in Python, pytest, and automation design patterns

🎯 Requirements

• Expert-Level Python including pytest (fixtures, plugins, parametrization), asyncio, and building scalable frameworks • Strong understanding of distributed systems: clustering, fault tolerance, horizontal scaling; experience with machine orchestration desirable • Extensive Linux experience (Ubuntu/RHEL) and knowledge of storage protocols (S3/Object, NVMe/iSCSI, NFS/SMB) • Experience integrating performance tools: fio, IOR, Minio Warp, Mongoose, MLPerf and orchestrating tests with Docker and Kubernetes • CI/CD expertise with Jenkins or GitHub Actions • Observability experience with Grafana, Prometheus, or ELK Stack • Experience applying AI/ML techniques to testing; familiarity with Pandas, NumPy, SciPy, scikit-learn preferred • Proficiency in Bash; bonus points for Go or C++ experience • Leadership and mentoring experience; excellent communication and strategic thinking; builder's mindset, commitment to quality