Deephaven Data Labs is a leading-edge data software provider. The primary product is an end-to end data platform used primarily by customers involved directly with the capital markets – hedge funds, securities trading operations, banks with direct market access. The product includes technologies related to data storage and federation, compute engines, proprietary tooling, and integrations with popular third-party data science libraries and experiences.
The company’s product is a Java application that runs on a variety of systems, including Windows, Linux, and macOS clients; and Linux servers, cloud systems, and containers. It has user experiences in Groovy and Python, respectively; but aspects of the platform have native integrations with C++, R, C#, and SQL workflows.
The Quality Assurance Engineer is responsible for working with the engineering, documentation, and customer-support teams to ensure the quality of deliverable products and documentation, and the characterization of discovered deficiencies.
- Designing and implementing large scale test environments to validate Deephaven products at scale comparable to that used by average customers.
- Designing and implementing an automated test framework that will perform installation validation and regression testing of new builds of Deephaven products.
- Developing system and integration test automations for existing and new features.
- Developing feature test plans.
- Ad-hoc and regression-driven testing.
- Installing and configuring respective server and client components to validate procedures and process-related documentation, and to simulate customer installs for reproduction of issues.
- Validating user-facing documentation.
- Writing requirements for needed documentation and bug fixes.
- Benchmarking execution times and resource utilization to validate performance.
Required Experience, Knowledge, and Skills:
- Familiarity with Linux operating system.
- Previous experience with a requirements/defect tracking system.
- Experience with administration of Java applications.
- Familiarity with scripting and/or other automation/orchestration tools.
- Experience with OS, process, and network monitoring tools.
- Experience with unit tests and unit test tools.
- Comfort in a driven, entrepreneurial setting.
- Posture and communication skills relevant for working within a high-paced team and interacting with customers.
- Experience with cloud and container infrastructure systems (Kubernetes, Docker, AWS, Google Cloud, etc)
- Some familiarity with enterprise level networking and storage systems (firewalls, SAN/NAS, etc.)