Senior Software Engineer - Data Infrastructure

What You Will be Doing:

• Design and implementation of complex distributed systems to ingest large volumes of data and implement state-of-the-art complex algorithms.
• Design and build APIs around AI/ML observability product functionality.
• Partner with machine learning engineers, infrastructure engineers, and product managers to design, build, and release new products and features.
• Participate in early customer engagements and PoCs, and use that context to drive new product features
• Review design and code, and make sure what we ship is awesome

• Someone who enjoys having significant ownership of features and systems and pursues results-driven development approaches consistent with pragmatism.
• Someone who builds systems that balance scalability, availability, and latency.
• An advocate for improving engineering efficiency, continuous deployment and automation tooling, monitoring solutions, and self-healing systems that enhance the developer experience.
• Good communication skills, mentoring, and a force-multiplying track record.
• Experience in building robust systems in production from the ground up.
• You have led and mentored others and care about the development of your teammates.
• You desire to learn and grow, push yourself and your team, share lessons with others, provide constructive and continuous feedback, and be receptive to feedback from others.

Minimum Requirements:

• BS in Computer Science or equivalent work experience
• 4+ years of proven track record in building and maintaining distributed data systems.
• Strong hands-on coding experience in Java, Python, or Go and comfortable diving into any new language or technology.
• Strong data infrastructure experience on-premise or Cloud Infrastructure
• Experience with some or similar or all of Flink, OLAP Datastores, Presto, Spark, and familiarity with key/value databases, Kafka, and Kubernetes.
• Experience working with modern cloud-based microservice architectures.

Nice-To-Haves:

• Experience building machine learning models or ecosystems
• Experience with Linux and containers using Docker and Kubernetes is a big plus.
• Having been a part of an engineering team at an early-stage startup