Java Developer Resume Keywords

What You Need to Know

Java developers work in enterprise environments where code written today needs to run for the next decade. Spring Boot has become the de facto standard, but understanding core Spring concepts like dependency injection and aspect-oriented programming separates competent developers from great ones. Maven and Gradle manage dependencies, but version conflicts between transitive dependencies cause headaches that take hours to resolve. Multithreading enables high performance but introduces race conditions that only appear under load, making them maddeningly difficult to reproduce and debug. Memory management is automatic through garbage collection, but OutOfMemoryErrors still happen, requiring heap dump analysis and JVM tuning. Legacy codebases often use Java 8 or older, meaning you can't use modern language features like records or pattern matching. Java development is synonymous with enterprise software—banks, insurance companies, healthcare systems, and government agencies rely on Java for mission-critical applications handling millions of transactions. This enterprise focus shapes Java's culture. Stability and backward compatibility take precedence over cutting-edge features. Code written in Java 8 still runs on Java 17 without modification. This stability is valuable for long-lived systems but frustrating for developers wanting to use modern language features. Understanding this enterprise context helps Java developers navigate the unique challenges of this ecosystem. Spring Framework dominates Java web development. Spring's dependency injection container manages object lifecycles and wires dependencies automatically, making code more testable and maintainable. But understanding inversion of control and how Spring's ApplicationContext works requires studying beyond just using annotations. Spring Boot simplifies configuration with sensible defaults and embedded servers, letting you build production-ready applications quickly. But when something goes wrong, you need to understand what Spring Boot configured automatically. Spring Data JPA abstracts database access beautifully, but it can generate inefficient N+1 queries if you don't understand how JPA fetches related entities. Spring Security handles authentication and authorization, but its filter chain and configuration can be intimidating initially. Hibernate is the most popular JPA implementation, translating Java objects to SQL automatically. But Hibernate has quirks that bite developers who don't understand its caching mechanisms. The first-level cache is enabled by default, caching entities within a session. The second-level cache spans sessions but requires careful configuration. Query caching caches query results but can cause stale data if not invalidated properly. Understanding lazy versus eager loading prevents N+1 query problems where fetching one entity triggers dozens of additional queries. Writing efficient Hibernate code requires understanding both SQL and how Hibernate generates SQL from your Java code. Multithreading in Java enables high-performance concurrent applications but requires deep understanding to get right. The java.util.concurrent package provides ExecutorService for thread pools, CountDownLatch for coordinating threads, and ConcurrentHashMap for thread-safe collections. But using these correctly requires understanding happens-before relationships, memory visibility, and the Java Memory Model. Race conditions occur when threads access shared mutable state without proper synchronization, causing bugs that only appear intermittently under load. Deadlocks happen when threads wait for locks held by each other, freezing the application completely. Modern Java introduces virtual threads through Project Loom, promising to simplify concurrent programming by making threads lightweight. Memory management through garbage collection frees developers from manual memory allocation, but that doesn't mean ignoring memory. Different garbage collectors—G1GC, ZGC, Shenandoah—have different characteristics affecting throughput and latency. OutOfMemoryErrors still occur, usually from memory leaks where objects are unintentionally retained in memory. Heap dumps capture memory state for analysis with tools like Eclipse Memory Analyzer, showing which objects consume memory. Understanding when objects become eligible for garbage collection and tuning JVM parameters for production workloads affects application performance and stability. Maven and Gradle are the primary build tools, managing dependencies and orchestrating builds. Maven uses XML configuration and follows convention over configuration, making simple projects straightforward. Gradle uses Groovy or Kotlin DSL, offering more flexibility at the cost of complexity. Both tools manage transitive dependencies—your code depends on library A, which depends on library B, which depends on library C. Version conflicts arise when different libraries require incompatible versions of the same dependency. Maven's dependency management and Gradle's dependency resolution strategies help, but resolving conflicts requires understanding dependency trees and sometimes excluding transitive dependencies manually. Microservices architecture is increasingly common in modern Java applications. Spring Cloud provides service discovery with Eureka, externalized configuration with Config Server, circuit breakers with Resilient4j, and distributed tracing with Sleuth. But microservices introduce operational complexity—service-to-service communication failures, distributed transactions, and consistency across services. Not every application benefits from microservices. The operational overhead of managing multiple services, deployments, and databases can outweigh benefits for smaller applications. Understanding when microservices make sense versus when a well-structured monolith is simpler requires experience.