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.

3. Key Responsibilities

A. OpenSearch Platform Architecture

• 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.

B. Kafka and Redis Reliability

• 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.

C. Automation, Observability, and Capacity Engineering

• 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.

D. Technical Strategy and Cross-Team Leadership

• 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.

4. Required Qualifications and Experience

• 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.

5. Preferred Qualifications

• 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.

6. Expected Outcomes in the First 6-12 Months

• Establish measurable reliability, capacity, lifecycle, and operational-maturity baselines for critical data-platform services and lead material improvements.

• Deliver reusable automation and observability patterns that reduce systemic toil and improve safe execution across teams.

• Publish and drive adoption of domain-level architecture, production-readiness, capacity, and resilience standards.

• Resolve one or more high-impact systemic risks and develop a prioritized multi-quarter improvement roadmap for the data-platform domain.

7. What This Role Does Not Include

• This is not a people-management role unless separately assigned; leadership is exercised through technical influence and mentorship.

• This is not a coordination-only role; the Staff DBRE must remain hands-on and capable of deep technical investigation.

• This role does not replace ownership held by application, SRE, infrastructure, network, storage, or cloud teams.

• This is not a ticket-queue role limited to incident response and routine operations; systemic improvement is expected.

• This role does not unilaterally define enterprise-wide cross-domain architecture or approval policy; that scope belongs to Principal-level and executive governance.

8. Work Expectations and Location

This role requires working during U.S. Eastern Time (ET) business hours and participating in on-call support as required.