Staff Database Reliability Engineer

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Staff Database Reliability Engineer (DBRE) - Data Platform OpenSearch Must-Have | Distributed Systems | Kafka | Redis | Capacity Engineering | Automation Level Staff DBRE (Senior Individual Contributor / Technical Lead) Domain Distributed Data Platforms Primary Focus Cross-team architecture and systemic reliability for OpenSearch-centered data platforms, with Kafka, Redis, capacity engineering, and Python automation Work Expectations U.S. Eastern Time business hours; on-call participation as required

1. Role Summary We are seeking an experienced Staff DBRE with deep OpenSearch/Elasticsearch expertise and strong distributed-systems judgment. The role leads high-impact architecture, reliability, performance, scalability, capacity, and lifecycle improvements across major data-platform services, complemented by Kafka, Redis, Linux, observability, and Python automation. The Staff DBRE solves ambiguous cross-team problems, establishes reusable platform patterns, mentors senior engineers, and influences multiple engineering groups. The scope extends beyond a single cluster or incident while remaining hands-on and evidence-driven. 2. Level Scope and Leadership Expectations Staff DBRE is a senior individual-contributor technical leadership role focused on solving ambiguous, high-impact reliability and architecture problems across platforms and teams. The role has broad influence within the data-platform domain, while enterprise-wide cross-domain strategy remains a Principal-level responsibility. Dimension Expectation at This Level Scope of Ownership Owns complex cross-team reliability, scalability, performance, capacity, architecture, and operational-maturity problems across major OpenSearch-centered platform services. Technical Depth Serves as a domain authority in OpenSearch and distributed-systems failure modes, with strong supporting depth in Kafka, Redis, JVM, Linux, storage, and networking. Engineering Leadership Defines platform-domain patterns, mentors Senior and Lead engineers, raises design-review and incident-analysis rigor, and influences adjacent teams. Automation Architecture Designs reusable Python automation, observability, validation, and lifecycle-management patterns rather than isolated scripts. Execution Drives ambiguous initiatives from problem framing through design, alignment, implementation guidance, and measurable outcomes. Organizational Influence Influences multiple engineering groups through data, production evidence, design standards, and technical judgment; does not unilaterally set enterprise strategy.