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# Distributed Systems Engineer

- **Company:** Bairesdev S.A.
- **Location:** Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Apache HTTP Server, Catalyst (Software), Cloud Engineering, Code Review, Software Debugging, Distributed Systems, Memory Management, Apache Hadoop, Apache Hive, Java Virtual Machine (JVM), Open Source Technology, Software Deployment, Software Engineering, Data Streaming, Parquet, Apache Spark, Parallel Computation, Kubernetes
- **Published:** August 11, 2026
- **Apply:** https://es.trabajo.org/oferta-4112-fc78750598fdd377ebbbbff96cd78a60

## About the Role

experience in software development. Strong Java and/or Scala skills. Experience with distributed systems and concurrent or parallel programming. Working knowledge of Spark internals (Catalyst, Tungsten, DAG Scheduler, Shuffle, or memory management). Familiarity with JVM performance characteristics (GC, memory, threading). Advanced level of English. Nice To Have Upstream contributions to Apache Spark (merged PRs in Spark SQL or Structured Streaming); committer status is a strong plus. Contributions to Apache Iceberg, Hive, Hadoop, Arrow, or Parquet. Kubernetes and cloud-native Spark deployment experience. Experience operating Spark at petabyte scale in production. How we do make your work (and your life) easier: Remote Work. Excellent compensation in USD or your local currency if preferred. Hardware and software setup for you to work from home. Flexible hours: create your own schedule. Paid parental leaves, vacations, and national holidays. Innovative and multicultural work

## Description

Apache Spark project and adjacent ecosystem projects (Iceberg, Hive, Hadoop, Arrow, Parquet), with ample scope for upstream contribution. We're looking for engineers who have worked on engine-level code in Spark or comparable distributed compute systems and are comfortable debugging concurrency, memory, and execution issues at scale. What You'll Do Contribute production-grade code to the Apache Spark project, particularly in Spark SQL and Structured Streaming. Debug and optimize Spark internals - Catalyst, Tungsten, DAG Scheduler, Shuffle, and memory management - at petabyte scale. Influence architectural direction for Spark performance and scalability. Profile and tune JVM behavior (GC, memory layout, concurrency) at the engine level. Collaborate with cross-functional engineering teams and open source committers on integrations and ecosystem work. Mentor senior engineers and raise the engineering bar through code reviews and design critiques. What We Are Looking For 6+ years of


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