Sr Staff Software Engineer (Distributed Systems | Data Ingestion | Storage | Analytics)

We are seeking a senior technical leader to shape the future of a large-scale health log analytics platform that powers intelligent operations and helps enterprise customers identify and resolve issues faster. In this role, you will influence the technical roadmap, guide architecture across data ingestion, storage, and query systems, and drive improvements in reliability, performance, and cost efficiency. You will collaborate closely with engineering, product, and customer-facing teams to turn complex challenges into scalable solutions. Through hands-on prototyping, thoughtful technical reviews, and evidence-based decision-making, you will help establish architectural direction across multiple teams. You will also champion practical, responsible AI adoption while mentoring senior engineers and developing future technical leaders. This is an opportunity to make a lasting impact on a mission-critical data platform in a collaborative, innovation-driven environment.

• Extensive distributed systems experience: 12+ years building and operating production distributed systems, including substantial ownership of large-scale data platforms or infrastructure. Equivalent depth demonstrated through sustained technical leadership is also valued.

• Broad data platform expertise: Strong understanding of data ingestion, storage, querying, and AI applications built on data, with deep expertise in at least one of these areas.

• Relevant data technologies: Experience with ingestion platforms such as Kafka, Pulsar, or Kinesis, and familiarity with technologies or architectures such as columnar databases, time-series databases, Elasticsearch/OpenSearch, and object storage.

• Cloud-native infrastructure knowledge: Experience operating Kubernetes and cloud infrastructure at scale, with an understanding of distributed systems reliability and operational complexity.

• Strong programming skills: Proficiency in at least one of Go, Java, Scala, Rust, or Python, with the ability to build prototypes, conduct detailed code reviews, and contribute directly to implementation when needed.

• Evidence-based AI judgment: Experience using AI-powered tools, automated workflows, or AI-driven insights in work processes and decision-making, or critically evaluating AI's impact on infrastructure. Demonstrated experience shipping AI-enabled solutions—or a well-developed, practical perspective on their application—is important.

• Responsible AI practices: Ability to evaluate AI solutions critically and implement appropriate safeguards for correctness, safety, reliability, and cost.

• Architectural and technical leadership: A demonstrated ability to influence technical direction across teams without relying on formal authority, supported by clear reasoning, credibility, and sound judgment.

• Trade-off management: Strong decision-making skills when balancing consistency, latency, cost, reliability, and operational burden.

• Communication and collaboration: Ability to explain complex technical concepts clearly to audiences ranging from early-career engineers to senior executives, while fostering constructive debate and shared ownership.

• People development: A humble, collaborative approach to leadership, with a commitment to mentoring others, sharing credit, and helping colleagues succeed.

• Adaptability and ownership: Comfort working with ambiguous problems, defining direction where requirements are incomplete, and turning early-stage ideas into robust, scalable systems.

• Remote leadership experience: Experience working effectively in remote environments and leading distributed teams.

• Nice to have: Background in observability, log analytics, OpenTelemetry, or AIOps; experience at an AI-native organization or building agents and LLM-based features; and familiarity with multitenant SaaS platforms and per-tenant isolation.