Senior Software Engineer — Distributed Compute / Spark Systems

Build distributed compute systems for large-scale analytical and AI workloads

Improve performance and cost efficiency across Spark, Trino, Presto, Flink, Databricks, and Snowflake-adjacent environments

Design workload-aware systems for query execution, resource allocation, scheduling, and compute optimization

Optimize execution performance across joins, aggregations, scans, shuffles, spills, caching, partitioning, and task scheduling

Build systems that learn from workload patterns and automatically improve execution plans, cluster usage, and compute efficiency

Develop infrastructure for adaptive workload routing, execution planning, and data-processing reliability across large customer environments

Debug performance bottlenecks across query execution, metadata, storage, network, memory, CPU, and distributed compute layers

Work with lakehouse tables and columnar formats such as Iceberg, Delta Lake, Hudi, Parquet, and ORC to improve end-to-end workload performance

Build systems that reduce compute waste caused by inefficient scans, poor partitioning, small files, skew, unnecessary shuffles, and suboptimal workload placement

Improve reliability and failure recovery for large distributed data-processing jobs

Implement algorithms in workload optimization, execution efficiency, cost modeling, and data-processing performance

Contribute to open-source or publish research when appropriate

Strong engineering depth in distributed systems, data processing systems, query engines, databases, or cloud infrastructure

Production experience with distributed compute or query systems such as Apache Spark, Spark SQL, Trino, Presto, Flink, Databricks, EMR, Glue, Hive, or similar systems

Hands-on experience improving performance, reliability, or cost efficiency for large-scale data-processing workloads

Understanding of distributed execution, query planning, scheduling, resource management, fault tolerance, and workload isolation

Experience with Spark internals, Spark SQL, Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling

Familiarity with lakehouse formats and columnar data such as Iceberg, Delta Lake, Hudi, Parquet, or ORC

Familiarity with cloud object storage systems such as S3, GCS, or ADLS and the performance tradeoffs of running distributed compute on top of them

Strong programming skills in Scala, Java, Go, Rust, C++, or similar systems-oriented languages

Curiosity about workload optimization, cost modeling, adaptive execution, and how compute efficiency affects AI and analytics at scale

A pragmatic builder’s mindset: rigorous, hands-on, and comfortable owning complex systems end to end

Experience contributing to Apache Spark, Spark SQL, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems

Experience with Catalyst, Adaptive Query Execution, cost-based optimization, query planning, vectorized execution, or distributed runtime systems

Experience optimizing joins, aggregations, shuffles, scans, spills, caching, partitioning, skew handling, or task scheduling

Experience building workload schedulers, execution control planes, resource managers, or multi-engine compute platforms

Experience reducing compute cost or improving workload efficiency in large-scale production data environments

Background in query engines, distributed runtimes, storage-aware execution, indexing, caching, encoding, compression, or adaptive query optimization

Research or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure

Build foundational infrastructure for enterprise data and AI

Work on deep systems problems across distributed compute, query execution, workload optimization, scheduling, resource management, and compute efficiency

Partner directly with Product, Engineering, and company leadership

Help shape Crunch, Granica’s production data optimization platform for enterprise-scale lakehouse environments

Work with a small, high-caliber team solving high-value infrastructure problems at massive scale

Have direct influence on architecture, product direction, customer outcomes, and company growth