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Mid-level generalist SDE title plus some niche Kafka/Spark requirements creates moderate competition density.
Strong streaming JVM backend skills transfer across industries moderately, but domain-specific Kafka/Spark expertise raises selectivity.
Explicit 3–8 years plus mandatory Kafka, Spark, Java, and distributed-systems expertise enforces strict filtering.
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Own end-to-end delivery of fault-management modules including low-level design, development, and production support within an AIOps platform.
Build and maintain near-real-time streaming pipelines using Kafka Streams, Kafka Connect, and Apache Spark meeting latency and fault tolerance SLAs.
Serve as primary escalation point for critical production incidents, lead debugging of distributed systems, and contribute to operational excellence including on-call and runbook creation.
3–8 years of hands-on backend or data engineering experience.
Strong expertise with Apache Kafka including production topics, partitioning, consumer groups, Kafka Streams DSL and Processor API, and Kafka Connect lifecycle management.
Experience building cloud-native microservices using Java 11+ and Spring Boot 3.x with knowledge of reactive patterns and distributed JVM system debugging.
Proficient in Apache Spark (Structured Streaming, DataFrames, Spark SQL) and understanding of distributed systems principles (CAP theorem, eventual consistency).
Experienced individual contributor skilled at owning technical modules end-to-end within complex distributed and event-driven systems.
Comfortable balancing velocity and reliability trade-offs and working closely with architects and product managers to deliver impactful features.
Familiarity or experience with AIOps platforms, telecom OSS/BSS domains, and agentic/LLM-assisted development workflows is a plus.