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

Accountabilities: Shape technical strategy and roadmap: Partner with engineering and product leadership to define the platform's long-term direction, balancing architectural vision with practical constraints and customer needs. Drive customer-centered innovation: Work directly with customers to understand evolving requirements, translate feedback into actionable product insights, and ensure the platform continues to deliver measurable value. Lead architecture across the data lifecycle: Establish technical direction for collection, ingestion, routing, enrichment, durable storage, retention, and high-performance querying across distributed systems and multiple engineering teams. Prototype and validate solutions: Document architectural proposals and build working prototypes that demonstrate feasibility, clarify trade-offs, and help teams align around technical decisions. Establish reliability and efficiency standards: Define and improve expectations for data completeness, durability, latency, availability, tenant isolation, and unit cost across the entire data pipeline. Guide complex technical transitions: Lead approaches to storage migrations, schema evolution, data replay, and platform upgrades while minimizing downtime and protecting data integrity. Set architectural boundaries and contracts: Define clear interfaces and responsibilities across teams working on collection, ingestion, storage, anomaly detection, querying, and AIOps experiences. Champion responsible AI adoption: Evaluate AI-driven opportunities based on customer value, data quality, reliability, safety, and cost. Run targeted experiments, validate outcomes, and identify which solutions merit broader investment. Raise engineering quality: Lead in-depth design reviews that strengthen technical decisions, encourage constructive discussion, and help engineers develop their capabilities. Promote operational excellence: Establish strong standards for code quality, on-call practices, incident response, and continuous learning from production issues. Mentor technical leaders: Coach senior engineers and technical leads, foster collaboration, and help develop the next generation of engineering leadership. Influence across teams: Build trust through clear communication, sound technical judgment, and collaborative leadership, including with teams outside your direct ownership. Requirements: 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. Benefits: Base salary: $171,900–$300,800 per year, depending on geographic location, qualifications, skills, competencies, and experience. Additional compensation: Equity where applicable and variable or incentive compensation, depending on the role and applicable compensation arrangements. Health coverage: Health plans and flexible spending accounts. Retirement savings: 401(k) plan with company matching. Employee stock purchase: Employee Stock Purchase Plan (ESPP). Giving back: Matching donations to support eligible charitable contributions. Time away: Flexible time-away arrangements. Family support: Family leave programs. Flexible work environment: Work arrangements may vary by role and assigned location, with flexible, remote, or office-based work personas where applicable. Technical impact and growth: Opportunities to solve complex, large-scale engineering challenges, shape platform architecture, work closely with enterprise customers, and help influence product direction. Collaborative culture: An environment that values clear thinking, honest feedback, continuous learning, and delivering meaningful results.