Senior Software Engineer — Distributed Compute / Spark Systems

Location: Mountain View, CA — On-siteAbout GranicaGranica builds AI infrastructure for enterprises operating massive data environments.Our platform helps data and engineering teams reduce storage and compute costs, improve performance and reliability, and prepare large datasets for analytics and AI.Granica’s Products IncludeCrunch — continuous optimization for enterprise lakehouse dataMyelin — stateful infrastructure for long-running AI agentsLarge Tabular Models — foundation models designed for enterprise tablesTogether, we are building the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.Granica has demonstrated approximately $200K in annualized value per petabyte and verified customer value within weeks.About The RoleGranica is hiring a Senior Software Engineer to build distributed compute systems for enterprise-scale data and AI workloads.You will work on the core infrastructure behind Crunch, Granica’s continuous optimization product for enterprise lakehouse data. This includes systems for distributed execution, workload optimization, query performance, scheduling, resource management, and compute cost reduction across petabyte- and exabyte-scale environments.You will own core systems that directly affect customer compute spend, query latency, workload reliability, cluster efficiency, and the performance of large-scale analytical data processing.This is a hands-on engineering role for someone who has deep systems experience and wants to build at the intersection of Spark, distributed query execution, lakehouse compute, workload scheduling, storage-aware optimization, and AI infrastructure.You will work on distributed compute systems involving Apache Spark, Spark SQL, Trino, Presto, Flink, Databricks, Snowflake-adjacent environments, cloud object stores, and lakehouse formats such as Apache Iceberg, Delta Lake, and Apache Hudi.What You’ll DoBuild distributed compute systems for large-scale analytical and AI workloadsImprove performance and cost efficiency across Spark, Trino, Presto, Flink, Databricks, and Snowflake-adjacent environmentsDesign workload-aware systems for query execution, resource allocation, scheduling, and compute optimizationOptimize execution performance across joins, aggregations, scans, shuffles, spills, caching, partitioning, and task schedulingBuild systems that learn from workload patterns and automatically improve execution plans, cluster usage, and compute efficiencyDevelop infrastructure for adaptive workload routing, execution planning, and data-processing reliability across large customer environmentsDebug performance bottlenecks across query execution, metadata, storage, network, memory, CPU, and distributed compute layersWork with lakehouse tables and columnar formats such as Iceberg, Delta Lake, Hudi, Parquet, and ORC to improve end-to-end workload performanceBuild systems that reduce compute waste caused by inefficient scans, poor partitioning, small files, skew, unnecessary shuffles, and suboptimal workload placementImprove reliability and failure recovery for large distributed data-processing jobsImplement algorithms in workload optimization, execution efficiency, cost modeling, and data-processing performanceContribute to open-source or publish research when appropriateWhat We’re Looking ForStrong engineering depth in distributed systems, data processing systems, query engines, databases, or cloud infrastructureProduction experience with distributed compute or query systems such as Apache Spark, Spark SQL, Trino, Presto, Flink, Databricks, EMR, Glue, Hive, or similar systemsHands-on experience improving performance, reliability, or cost efficiency for large-scale data-processing workloadsUnderstanding of distributed execution, query planning, scheduling, resource management, fault tolerance, and workload isolationExperience with Spark internals, Spark SQL, Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task schedulingFamiliarity with lakehouse formats and columnar data such as Iceberg, Delta Lake, Hudi, Parquet, or ORCFamiliarity with cloud object storage systems such as S3, GCS, or ADLS and the performance tradeoffs of running distributed compute on top of themStrong programming skills in Scala, Java, Go, Rust, C++, or similar systems-oriented languagesCuriosity about workload optimization, cost modeling, adaptive execution, and how compute efficiency affects AI and analytics at scaleA pragmatic builder’s mindset: rigorous, hands-on, and comfortable owning complex systems end to endBonusExperience contributing to Apache Spark, Spark SQL, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systemsExperience with Catalyst, Adaptive Query Execution, cost-based optimization, query planning, vectorized execution, or distributed runtime systemsExperience optimizing joins, aggregations, shuffles, scans, spills, caching, partitioning, skew handling, or task schedulingExperience building workload schedulers, execution control planes, resource managers, or multi-engine compute platformsExperience reducing compute cost or improving workload efficiency in large-scale production data environmentsBackground in query engines, distributed runtimes, storage-aware execution, indexing, caching, encoding, compression, or adaptive query optimizationResearch or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructureWhy Join GranicaBuild foundational infrastructure for enterprise data and AIWork on deep systems problems across distributed compute, query execution, workload optimization, scheduling, resource management, and compute efficiencyPartner directly with Product, Engineering, and company leadershipHelp shape Crunch, Granica’s production data optimization platform for enterprise-scale lakehouse environmentsWork with a small, high-caliber team solving high-value infrastructure problems at massive scaleHave direct influence on architecture, product direction, customer outcomes, and company growthCompensation & BenefitsCompetitive salary, meaningful equity, and performance bonus for top performers401(k) with company match, comprehensive health coverage, and unlimited PTODaily catered meals in our Mountain View officeSupport for research, publication, and conference participationAt Granica, you'll help build the next generation of enterprise AI—from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we're creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.Compensation Range: $160K - $240K