This is an incredible opportunity to be part of a company that has been at the forefront of AI and high performance data storage innovation for over two decades.

DataDirect Networks (DDN) is a global market leader renowned for powering many of the world's most demanding AI data centers, in industries ranging from life sciences and healthcare to financial services, autonomous cars, Government, academia, research and manufacturing.

Our commitment to innovation, customer success, and market leadership makes this an exciting and rewarding role for a driven professional looking to make a lasting impact in the world of AI and data storage.

Principal Architect Quality Engineering Framework.

About the Role :

As Principal Architect for Quality Engineering Frameworks, you will own the technical vision, architecture, and evolution of Infinias pytest based automation platform.

This role is responsible for transforming automation into a scalable, developer centric service that validates correctness, performance, resilience, and real- world behavior across highly distributed systems.

This is a hands- on, staff plus individual contributor role with broad organizational influence.

You will write and review production- quality code, define architectural standards, and mentor engineers across QE and Development.

Responsibilities :

Framework Architecture :

- Own the end- to- end architecture and technical direction of the Python/pytest automation framework.

- Define architectural standards, extension points, and long-term evolution.

- Make and document architectural tradeoffs through design reviews and Architectural Decision Records.

- Ensure the framework scales with system complexity and organizational growth.

Automation Platform and Reusable Tooling :

- Design and implement reusable Python libraries, pytest fixtures, and plugins.

- Provide a self service automation platform with a clear, opinionated paved road for developers.

- Enable testing of APIs, CLIs, storage systems, and distributed workloads using shared abstractions.

- Maintain strict standards for determinism, readability, and maintainability.

System Correctness, POSIX, S3, and Storage Validation :

- Architect automation validating POSIX filesystem semantics, including metadata operations, locking, concurrency, permissions, and consistency.

- Validate object, block, and networked storage systems including S3 compatible object storage, NVMe/iSCSI, and NFS/SMB.

- Ensure correctness under failure scenarios, scale, and sustained load.

Distributed Systems, Resilience, and Scale :

- Design automation covering clustering behavior, membership changes, failover, and recovery.

- Validate horizontal and elastic scaling in real deployment conditions.

- Extend automation into repeatable resilience and chaos testing beyond simple failure injection.

Performance and Stress Testing :

- Integrate performance and stress testing into CI/CD pipelines.

- Use tools such as fio, IOR, Minio Warp, Mongoose, and MLPerf.

- Validate throughput, latency, and stability and continuously detect performance regressions.

Cloud and Execution Environments :

- Architect automation to execute consistently across AWS and GCP.

- Support execution on Kubernetes, Docker, hypervisors, and bare- metal systems.

- Validate cloud- specific behaviors including autoscaling, contention, networking variability, and zonal failure modes.

- Balance execution scale, cost, and feedback time.

Telemetry Driven Validation :

- Integrate automation with Grafana, Prometheus, and ELK.

- Validate system behavior using metrics and logs in addition to test assertions.

- Enable deep diagnostics and root- cause analysis from automated runs.

Code Quality and Technical Leadership :

- Lead code reviews for framework and automation contributions.

- Enforce architectural and coding standards across the automation repository.

- Act as a technical authority for test design and framework usage.

- Mentor QE and Development engineers in Python, pytest, and automation architecture.