Back
Mid-level SDE title and 3–8 year band increase applicant density despite specialized Kafka/Spark requirements.
Requires deep Kafka, streaming, and distributed-systems expertise, limiting cross-industry transferability.
Explicit 3–8 years plus mandatory Kafka, Spark, and Java expertise enforces strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end delivery of one or more fault-management modules within an AIOps platform, including design, development, and production support.
Build near-real-time streaming pipelines using Kafka Streams, Kafka Connect, and Apache Spark to meet SLA targets for latency, throughput, and fault tolerance.
Serve as primary escalation point for critical production incidents and actively incorporate AI-assisted development and ML-powered capabilities into fault resolution workflows.
3–8 years of backend or data engineering experience.
Strong expertise in Kafka internals, including Kafka Streams DSL and Processor API, Kafka Connect, offset management, and distributed systems principles.
Proficient in Java 11+ and Spring Boot 3.x with experience building cloud-native microservices.
Experience with Apache Spark Structured Streaming and debugging complex distributed JVM systems.
Experienced individual contributor comfortable owning technical decisions and module-level trade-offs between velocity and reliability.
Strong background in distributed systems and streaming data pipelines in production environments, familiar with multi-threaded JVM system debugging.
Experience or interest in AIOps, agentic/LLM-assisted development workflows, telecom OSS/BSS domain, or cloud-native deployments to strategically align with platform evolution.