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.
