Spark Engineer

Use when building Spring Boot 3.x applications, microservices, or reactive Java applications. Invoke for Spring Data JPA, Spring Security 6, WebFlux, Spring Cloud integration.

Use when defining SLIs/SLOs, managing error budgets, or building reliable systems at scale. Invoke for incident management, chaos engineering, toil reduction, capacity planning.

Use when building real-time communication systems with WebSockets or Socket.IO. Invoke for bidirectional messaging, horizontal scaling with Redis, presence tracking, room management.

Design and operate scalable data pipelines and architectures using best-fit patterns, tools, and modeling methodologies without external dependencies.

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

Use when understanding legacy or undocumented systems, creating documentation for existing code, or extracting specifications from implementations. Invoke for legacy analysis, code archaeology, undocumented features.