Requirements

• 4+ years of professional software engineering experience building and operating production systems.
• Engineering background in Computer Science, Mathematics, Software Engineering, Physics, or a similar field, or equivalent practical experience.
• Strong coding skills with demonstrated proficiency in one or more languages such as Java, Rust, Scala, or C++.
• Experience developing database engines, distributed data processing systems, storage systems, or comparable infrastructure, with depth in areas such as query planning, execution, or performance optimization.
• Strong foundations in algorithms, data structures, and concurrency, with experience diagnosing correctness and performance problems in complex systems.
• Strong written and verbal communication skills and the ability to work effectively across teams, incorporate feedback, and hold a high bar for quality.

What We Value

• Ownership mindset and a high bar for correctness. Our systems support decisions and operations that customers depend on.
• Curiosity about how data systems work, from query optimizers and execution operators to table formats and distributed processing.
• Strong debugging skills and motivation to follow a problem across languages, services, and layers of the stack.
• A practical approach to performance, grounded in profiling, representative workloads, and measurable improvements.
• Interest in applying deep systems engineering to real-world problems, with empathy for the people who use and depend on our software.
• Experience building or extending systems such as Spark, DataFusion, Iceberg, or comparable technologies, and an interest in learning across the stack.
• Ability to collaborate across teams and work effectively with the open-source projects we build on. Experience contributing to open-source projects is valued, but not required.

Core Responsibilities

• Designing and implementing query planning and optimization capabilities that turn complex computations into efficient execution plans
• Extending execution engines with new capabilities and improving query operators, parallelism, memory management, and data movement
• Developing Foundry’s Iceberg catalog and engine integrations, including table metadata, transactions, and efficient reads and writes
• Building shared transformation semantics and execution interfaces for workloads across Foundry’s products and platform services
• Improving incremental processing so pipelines can reuse previous results and process new data efficiently while preserving correctness
• Evaluating and integrating advances in open-source data systems, validating their behavior and performance against real-world workloads
• Investigating correctness and performance issues across planning, execution, and storage, and building tests and benchmarks that prevent regressions
• Working with product teams and customers to translate operational needs into engine capabilities that integrate with Foundry’s security, data management, and build infrastructure

Technologies We Use

• Java, Scala, Rust, and Python
• Apache Spark, Apache DataFusion, Apache Comet, and Velox for data processing and query execution
• Apache Iceberg for table management and catalog interoperability
• Apache Arrow and Apache Parquet for in-memory data processing and columnar storage
• Industry-standard build tooling, including Gradle, Cargo, and GitHub.