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Metro mid-level backend role with common title and specialized streaming stack yields medium competition.
Streaming and backend skills transfer across industries, but AIOps/telecom domain knowledge adds moderate specialization.
Explicit 3–8 years plus mandatory Kafka, Spark, and Java requirements create high selection strictness.
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Own and deliver one or more fault-management modules end-to-end, including low-level design, feature implementation, and production support.
Build and maintain near-real-time streaming data pipelines using Kafka Streams, Kafka Connect, and Apache Spark to meet SLA targets for latency, throughput, and fault tolerance.
Act as primary escalation point for critical production incidents in modules, debugging complex distributed systems and contributing to operational excellence including testing, observability, and CI/CD improvements.
3–8 years of hands-on backend/data engineering experience.
Strong expertise with Apache Kafka (topics, partitioning, consumer groups, offset management) and Kafka Streams (DSL, Processor API).
Proficiency in Java 11+ and Spring Boot 3.x for building cloud-native microservices.
Deep knowledge of distributed systems concepts and ability to debug distributed JVM systems.
Experienced individual contributor capable of end-to-end module ownership in complex fault management within AIOps or similar observability platforms.
Strong backend engineering skills focused on distributed systems, streaming pipelines, and microservices development with emphasis on reliability and scalability trade-offs.
Comfortable leveraging advanced AI-assisted development tools and integrating ML-powered capabilities into operational workflows.