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Tier-1 brand, generic Software Engineer title, and metro/hybrid location increase candidate competition.
Backend and distributed-systems expertise is transferable across industries, though platform specifics slightly limit fit.
Explicit seniority and many mandatory backend, distributed systems, and database skills raise filtering strictness.
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.
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.
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.