Software Engineer, Data & Scalability Platform (8-12 Yrs)
Cisco (company site, opens in a new tab)8–12 yrsBengaluruHybridDirect career pageEst. 30–55 LPA
Posted on 1 Sept
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at Cisco
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Cisco you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own end-to-end design, implementation, testing, and production support of high-scale backend services and data pipelines handling spans, metrics, and evaluation results.
- Enhance and maintain platform data stores (relational, analytical/columnar, object storage, caching) including schema changes and safe migrations.
- Drive performance, reliability, scalability, and fault tolerance improvements along data processing pipelines and participate in production debugging and optimization activities.
Minimum Requirements
- Bachelor's degree with 4+ years experience or Master's with 2+ or PhD with 0 years in related software engineering.
- Strong backend engineering experience building production-grade, large-scale distributed systems focusing on scalability, reliability, and performance.
- Experience with at least one database type (relational or analytical) including schema design, query tuning, and migrations; and streaming or queueing systems like Kafka, RabbitMQ, Pulsar, or Kinesis.
- Proficiency in Python and at least one other backend language (Go, Java, C++, etc.); solid fundamentals in system design, distributed systems, concurrency, data structures, algorithms.
Ideal Candidate Profile
- Experienced in building and optimizing large-scale distributed backend systems and data pipelines with practical exposure to multiple data store types and streaming platforms.
- Focused on measurable impact in system scalability, performance tuning, reliability, and observability instrumentation in production environments.
- Comfortable leading feature life cycle end-to-end including debugging production issues, collaborating on code reviews, and engaging in automated testing and CI/CD practices.