Spark Developer

InfraCloud

Remote· senior

This is a senior engineering role that puts you directly on open-source infrastructure work. If you want to understand how distributed data systems actually work—not just use them—this gets you there fast. You'll contribute to Apache Spark itself, the framework thousands of teams rely on.

You'll own real responsibilities: writing code that goes into the Spark project, optimizing its internals for production use cases, debugging complex performance issues, and designing scalable data processing solutions. You'll also work across product and platform teams to shape technical direction. This isn't a junior role—it assumes you can execute independently and lead architecture discussions.

This fits engineers with strong fundamentals in distributed systems, a proven track record shipping code (internships, side projects, or portfolio work count), and genuine curiosity about how databases and data frameworks operate under the hood. Computer science, engineering, or adjacent backgrounds help, but what matters is demonstrated technical depth.

InfraCloud is hiring for their India location on a remote basis. Apply directly through CareerJumpShip to connect with the hiring team and learn more about the specifics of the role and team structure.

About this role

About the Role We are looking for a highly skilled Senior Software Engineer with deep expertise in Apache Spark and distributed data processing systems. In this role, you will work directly on Apache Spark internals, contribute upstream improvements to the Spark open-source community, and adapt Spark capabilities to support DataPelago’s product requirements. This position demands strong technical ownership, independent execution, and the ability to drive high-impact engineering initiatives in a fast-paced environment. Key Responsibilities Contribute code, fixes, and enhancements directly to the Apache Spark open-source project. Upgrade and maintain compatibility with newer Apache Spark community releases. Analyze, modify, and optimize Spark internals to support DataPelago’s platform requirements. Design and implement scalable distributed data processing solutions. Debug and resolve complex performance, stability, and scalability issues within Spark-based systems. Collaborate with product and platform teams to align Spark capabilities with business and technical objectives. Drive architecture discussions and provide technical leadership across distributed systems initiatives. Ensure high engineering standards through code reviews, testing, documentation, and best practices. Work independently with minimal supervision while delivering high-quality outcomes. Required Qualifications Strong experience with Apache Spark internals and distributed computing systems. Proven experience contributing to open-source projects, preferably Apache Spark or related Apache ecosystem technologies. Expertise in Java and/or Scala programming. Strong understanding of query execution, distributed processing, memory management, and performance optimization. Experience upgrading and maintaining large-scale Spark deployments. Deep knowledge of big data technologies and distributed systems architecture. Strong debugging, problem-solving, and performance tuning skills. Ability to work autonomously and lead technically challenging initiatives. Preferred Qualifications Experience with query engines, vectorized execution, or data processing frameworks. Familiarity with Kubernetes, cloud-native environments, and large-scale infrastructure. Knowledge of JVM performance tuning and low-level system optimization. Prior experience working closely with open-source communities. What We Expect Senior-level ownership and accountability. High independence with the ability to make impactful technical decisions. Strong communication and collaboration skills. Passion for open-source software and distributed data technologies. Nice to Have Active GitHub or Apache contributor profile. Experience with large-scale analytics or database systems. Publications, talks, or community involvement in big data technologies. Originally posted on Himalayas

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