Staff Software Engineer, Data & Scalability Platform

Cisco • San Francisco, CA

6d • $186,900 - $267,700 • Hybrid

About The Position

The Staff Software Engineer on Cisco’s Data & Scalability Platform team provides technical leadership for the architecture, scalability, and cost of the data plane, including data stores, streaming and compute components, and tooling to evaluate system behavior under load. The role defines the multi-quarter technical direction and platform standards, leads initiatives to maintain performance and correctness as the platform scales, and influences platform decisions in partnership with engineering leadership and product teams. The team builds scalable, cost-effective evaluation and guardrails to help ensure AI agents behave as intended, improving reliability and reducing risk while supporting customers’ AI-powered applications with greater observability and control.

This is a hybrid position requiring on-site work Tuesdays and Thursdays. In California, Tuesdays are on-site in San Francisco and Thursdays in San Jose; in New York City, both days are on-site in the NYC office. The application window is expected to close on October 31, 2026.

Requirements

• Bachelor’s degree with at least 8 years of related experience, or a Master’s degree with at least 6 years, or a PhD with at least 3 years, in Computer Science, Software Engineering, or a related field.
• At least 6 years building and/or operating data-intensive backend services and/or platforms.
• Technical leadership experience, including setting technical direction, driving multi-quarter initiatives, and/or influencing decisions beyond the immediate scope.
• Experience designing, operating, and/or delivering highly scalable, reliable, production-grade data-intensive platforms.
• Experience designing large-scale distributed systems and solving scalability, availability, performance, reliability, and/or fault-tolerance problems.
• Backend software engineering experience with one or more of Go, Java, Rust, C++, or Python.
• Experience with cloud-native technologies, containerized environments such as Kubernetes, and public cloud platforms such as AWS or GCP.

Nice To Haves

• Demonstrated performance-engineering ability, including profiling, benchmarking, and load testing real systems and using measurements to inform capacity, design, and cost decisions.
• Experience operating and tuning both an OLTP database, such as PostgreSQL or MySQL, and an analytical, columnar, or time-series store, such as ClickHouse, Druid, BigQuery, or Snowflake, at production scale, including schema design, indexing, query tuning, and migration safety.
• Deep production experience with streaming or queueing systems such as Kafka, RabbitMQ, Pulsar, or Kinesis, including partitioning, consumer-group semantics, delivery guarantees, backlog handling, and dead-letter handling.
• Experience scaling data-processing compute, such as queue consumers, asynchronous task workers, or streaming jobs, including concurrency tuning, batching, backpressure, and throughput under sustained load.
• Deep experience running ClickHouse or a comparable columnar store at scale, including sharding and replication topology, materialized views, merge and mutation behavior, and retention and TTL mechanics.
• Experience with a platform-level capacity model or FinOps practice, including cost per unit of telemetry, cardinality governance, and quota or rate-limit design.
• Experience designing multi-tenant isolation and noisy-neighbor mitigation in shared data systems.
• Experience leading a significant production data migration or storage-engine transition without customer-visible impact.
• Experience defining SLOs for data services and driving an organization to meet them.
• Experience with backup and restore, tested disaster-recovery drills against stated RPO/RTO, and data retention or deletion compliance such as GDPR/DSR hard deletion.
• Experience building observability instrumentation and pipelines, such as OpenTelemetry, collectors, and metrics or tracing backends, rather than only consuming dashboards.
• Experience building or operating performance tooling for other engineers, such as load-test harnesses, continuous profiling and flame-graph workflows (e.g., Pyroscope), or benchmarking suites.
• Experience with distributed task frameworks and worker orchestration, such as Celery, Temporal, Flink, or custom consumer fleets, at production scale.
• Familiarity with streaming databases, stream-processing frameworks, or distributed query engines such as RisingWave, Flink, Spark Streaming, or Trino/Presto, and with model-serving infrastructure.
• Experience supporting stateful systems in both cloud/SaaS and air-gapped or on-premises deployments.
• Experience mentoring senior engineers and raising engineering standards across multiple teams.
• Strong communication skills, including the ability to turn performance, capacity, and cost findings into clear recommendations for engineering leadership and product partners.

Responsibilities

• Define and drive the multi-quarter technical roadmap for the data platform, including storage, streaming, compute, and measurement tooling.
• Lead the architecture and evolution of the data plane, including storage engine selection, sharding and tenancy models, and control-plane/data-plane boundaries.
• Establish platform-wide standards for schema design, migration safety, and data retention, and lead the review process that enforces them.
• Lead capacity and performance modeling and define service-level objectives (SLOs) for correctness and speed.
• Lead major scalability and performance initiatives across data stores, streaming pipelines, and distributed worker fleets.
• Drive cost efficiency, including cost per unit of telemetry, cost attribution, and quota and rate-limit design.
• Set the direction for multi-tenant isolation to prevent one tenant from degrading another.
• Design and build internal load-testing, profiling, and benchmarking frameworks and performance tools that engineers use to support design and capacity decisions.
• Establish platform observability standards so that signals are correct, complete, and affordable.
• Lead major production investigations and drive systemic remediation rather than isolated fixes.
• Make and document technology-selection and build-versus-buy decisions, including explicit cost, risk, and migration analysis.
• Partner with engineering leadership to influence platform direction and investment, and represent the data platform to product teams.
• Mentor engineers and raise engineering standards through design reviews, technical standards, and documentation.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) plan with Cisco matching contribution
• Paid parental leave
• Short- and long-term disability coverage
• Basic life insurance
• Paid holidays
• Floating holiday for non-exempt employees
• Paid day off for employee’s birthday
• Paid year-end holiday shutdown
• Paid personal wellness days
• Paid vacation or flexible vacation time off, depending on employee classification
• Paid sick time
• Additional paid time away may be requested for critical or emergency family issues
• Optional paid volunteer days
• Annual bonuses for eligible non-sales roles
• Potential grants of Cisco restricted stock units, subject to eligibility and vesting terms

What This Job Offers

• Job Type: Full-time
• Career Level: Senior