Join us if you want to advance the capabilities of modern data systems and apply that work to consequential problems.
- 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
- 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
- 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. What We Require 4+ years of professional software engineering 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. Operating on top of these technologies alone does not qualify.
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.
