Staff Database Reliability Engineer

Overview

Staff Database Reliability Engineer (DBRE) in the Data Platform team, a senior individual contributor and technical lead role focused on cross-team architecture and systemic reliability for OpenSearch-centered data platforms, with Kafka, Redis, capacity engineering, and Python automation.

What you'll do

• Design and improve large-scale OpenSearch/Elasticsearch topology, dedicated node roles, shard and index strategies, lifecycle management, workload distribution, and recovery patterns.
• Solve complex indexing, search, cluster-state, JVM/GC, storage, segment, mapping, allocation, and cross-cluster reliability problems.
• Establish domain-level resilience, snapshot, replication, upgrade, and capacity patterns appropriate to the actual scale of the environment.
• Guide Kafka topology, partitioning, replication, capacity, consumer reliability, and failure-mode remediation for critical services.
• Guide Redis Cluster and Sentinel architecture, memory and eviction behavior, persistence tradeoffs, failover, re-sharding, and client reliability.
• Architect reusable Python tooling and safe remediation frameworks for provisioning, validation, maintenance, scaling, and incident prevention.
• Define domain-level observability, SLIs/SLOs, capacity models, lifecycle forecasts, and actionable risk thresholds.
• Establish Infrastructure-as-Code and operator patterns where they improve repeatability and supportability.
• Lead architecture and production-readiness reviews for the data-platform domain.
• Evaluate emerging technologies through workload-relevant benchmarks, failure testing, operability reviews, and total-cost considerations.
• Lead complex technical investigations and systemic corrective actions; mentor engineers and improve engineering practices across teams.

What you'll need

• 8+ years of DBRE, SRE, database, or distributed data-platform engineering experience with Staff, Lead, or comparable technical leadership scope.
• Deep hands-on mastery of OpenSearch and/or Elasticsearch internals, operations, performance, lifecycle management, recovery, and architecture.
• Strong production experience with Kafka and/or Redis, with the ability to reason across distributed data-platform failure modes.
• Strong systems expertise across JVM, Linux, CPU, memory, storage I/O, networking, and capacity behavior.
• Strong Python software-engineering capability for reusable production automation and tooling.
• Demonstrated success solving ambiguous cross-team technical problems and influencing architecture through evidence and judgment.
• Strong incident leadership, written communication, mentoring, and design-review skills.

Nice to have

• Experience with both Kafka and Redis at meaningful production scale.
• Kubernetes operators, Terraform, Ansible, GitOps, and cloud-managed data services.
• Experience evaluating ClickHouse, Pinot, vector databases, or stream-processing technologies.
• Experience with chaos testing, production-readiness reviews, and cost-aware capacity engineering.

Details

• Work during U.S. Eastern Time (ET) business hours.
• Participate in on-call support as required.
• Not a people-management role unless separately assigned; leadership is exercised through technical influence and mentorship.
• Does not unilaterally define enterprise-wide cross-domain architecture or approval policy; that scope belongs to Principal-level and executive governance.