Software Engineer - Data & Scalability P,latform

Minimum Qualifications

• Bachelor's degree with 7+ years of related experience, or Master's degree with 4+ years, or PhD with 1+ year of related experience, in Computer Science, Software Engineering, or a related field.
• Strong backend software engineering experience building, operating, and delivering highly scalable, reliable, production-grade data-intensive services and platforms.
• Proven experience with large-scale distributed systems, including designing and solving complex problems of scalability, availability, performance, reliability, and fault tolerance.
• Hands-on experience operating and tuning both an OLTP database (e.g., PostgreSQL, MySQL) and an analytical, columnar, or time-series store (e.g., ClickHouse, Druid, BigQuery, Snowflake) — schema design, indexing, query tuning, and migration safety.
• Production experience with streaming or queueing systems (e.g., Kafka, RabbitMQ, Pulsar, Kinesis) — partitioning, consumer-group semantics, delivery guarantees, and backlog and dead-letter handling.
• Experience scaling data-processing compute — queue consumers, async task workers, or streaming jobs — including concurrency tuning, batching, backpressure, and throughput behavior under sustained load.
• Strong proficiency in Python and solid experience with at least one additional backend programming language such as Go, Java, C++, or similar.
• Demonstrated performance engineering ability — profiling, benchmarking, and load testing real systems, and turning the measurements into capacity, design, and cost decisions.
• Experience with cloud-native technologies, containerized environments (Kubernetes), and public cloud platforms (AWS, GCP, or similar).
• Demonstrated ability to independently design, develop, debug, test, and maintain software with minimal guidance.

Preferred Qualifications

• Experience running ClickHouse or a comparable columnar store at scale — sharding and replication topology, materialized views, merge and mutation behavior, retention and TTL mechanics.
• Experience owning a capacity model or a FinOps practice: cost per unit of telemetry, cardinality governance, and quota or rate-limit design.
• Experience with multi-tenant isolation and noisy-neighbor mitigation in shared data systems.
• Experience with backup and restore, tested disaster-recovery drills against stated RPO/RTO, and data retention or deletion compliance (e.g., GDPR/DSR hard delete).
• Experience building observability instrumentation and pipelines (OpenTelemetry, collectors, metrics/tracing backends) rather than only consuming dashboards.
• Experience building or operating performance tooling as a product for other engineers — load-test harnesses, continuous profiling (e.g., Pyroscope, py-spy, flame-graph workflows), or benchmarking suites used to justify capacity and design decisions.
• Experience with distributed task frameworks and worker orchestration (e.g., Celery, Temporal, Flink, custom consumer fleets) at production scale.
• Familiarity with streaming databases, stream processing frameworks, or distributed query engines (e.g., RisingWave, Flink, Spark Streaming, Trino/Presto) and with model-serving infrastructure.
• Experience supporting stateful systems in both cloud/SaaS and air-gapped or on-prem deployments.
• Experience leading medium-sized features or projects end-to-end, mentoring engineers, and influencing technical decisions across teams.
• Strong communication skills, with the ability to turn complex performance, capacity, and cost findings into clear recommendations for engineering and product partners.

Why Cisco?

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