Principal Engineer - Compute Platform
ConfluentFull Time
Expert & Leadership (9+ years)
The ideal candidate possesses over 10 years of hands-on experience in software and infrastructure engineering, with a proven track record of designing, building, and scaling distributed, cloud-native systems. Demonstrated experience as a technical leader or architect, making key decisions on system design, scalability, performance, and cost optimization is required. Strong proficiency in API-first design, including REST, GraphQL, and OpenAPI specifications, is essential. Proficiency in TypeScript and Python is also necessary.
The Lead Platform Engineer will architect and evolve the cloud-native Tetra data platform to support high-throughput, low-latency data processing and customer-facing features. Responsibilities include designing scalable, distributed systems for authentication, authorization, data lifecycle management, search, operational intelligence, and real-time event processing. The role involves proactively analyzing platform performance and scalability, identifying constraints, and defining growth strategies. Collaboration with engineering and product teams to deliver supporting infrastructure for new services and applications is key. Building and maintaining infrastructure-as-code for automated, standardized, and secure deployments, enhancing observability and monitoring, and championing best practices in distributed systems design, scalability, and performance optimization are also core duties.
Cloud platform for scientific data management
TetraScience offers a cloud-based platform called the Scientific Data Cloud, which helps biopharmaceutical companies manage and harmonize their scientific data for research and development, quality assurance, and manufacturing. The platform connects various lab instruments and software, streamlining data management and significantly reducing task completion time. TetraScience's vendor-neutral and open design allows it to work with any lab equipment, making it a flexible solution in the life sciences sector. The company's goal is to enhance scientific outcomes by preparing data for artificial intelligence and machine learning applications.