{"schemaVersion":"jobsearcher.job.v1","id":"f6a33862bdced8fd38cdd3d9","url":"https://jobsearcher.com/jobs/f6a33862bdced8fd38cdd3d9","canonicalUrl":"https://jobsearcher.com/jobs/f6a33862bdced8fd38cdd3d9","title":"Senior Software Engineer — Distributed Compute / Spark Systems","description":"Senior Software Engineer — Distributed Compute / Spark Systems\n\nLocation: Mountain View, CA — On-site\n\nAbout Granica\n\nGranica builds AI infrastructure for enterprises operating massive data environments.\n\nOur platform helps data and engineering teams reduce storage and compute costs, improve performance and reliability, and prepare large datasets for analytics and AI.\n\nGranica’s products include:\n\nCrunch — continuous optimization for enterprise lakehouse data\n\nMyelin — stateful infrastructure for long-running AI agents\n\nLarge Tabular Models — foundation models designed for enterprise tables\n\nTogether, we are building the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.\n\nGranica has demonstrated approximately $200K in annualized value per petabyte and verified customer value within weeks.\n\nAbout the Role\n\nGranica is hiring a Senior Software Engineer to build distributed compute systems for enterprise-scale data and AI workloads.\n\nYou 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.\n\nYou 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.\n\nThis 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.\n\nYou 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.\n\nWhat You’ll Do\n\nBuild distributed compute systems for large-scale analytical and AI workloads\n\nImprove performance and cost efficiency across Spark, Trino, Presto, Flink, Databricks, and Snowflake-adjacent environments\n\nDesign workload-aware systems for query execution, resource allocation, scheduling, and compute optimization\n\nOptimize execution performance across joins, aggregations, scans, shuffles, spills, caching, partitioning, and task scheduling\n\nBuild systems that learn from workload patterns and automatically improve execution plans, cluster usage, and compute efficiency\n\nDevelop infrastructure for adaptive workload routing, execution planning, and data-processing reliability across large customer environments\n\nDebug performance bottlenecks across query execution, metadata, storage, network, memory, CPU, and distributed compute layers\n\nWork with lakehouse tables and columnar formats such as Iceberg, Delta Lake, Hudi, Parquet, and ORC to improve end-to-end workload performance\n\nBuild systems that reduce compute waste caused by inefficient scans, poor partitioning, small files, skew, unnecessary shuffles, and suboptimal workload placement\n\nImprove reliability and failure recovery for large distributed data-processing jobs\n\nImplement algorithms in workload optimization, execution efficiency, cost modeling, and data-processing performance\n\nContribute to open-source or publish research when appropriate\n\nWhat We’re Looking For\n\nStrong engineering depth in distributed systems, data processing systems, query engines, databases, or cloud infrastructure\n\nProduction experience with distributed compute or query systems such as Apache Spark, Spark SQL, Trino, Presto, Flink, Databricks, EMR, Glue, Hive, or similar systems\n\nHands-on experience improving performance, reliability, or cost efficiency for large-scale data-processing workloads\n\nUnderstanding of distributed execution, query planning, scheduling, resource management, fault tolerance, and workload isolation\n\nExperience with Spark internals, Spark SQL, Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling\n\nFamiliarity with lakehouse formats and columnar data such as Iceberg, Delta Lake, Hudi, Parquet, or ORC\n\nFamiliarity with cloud object storage systems such as S3, GCS, or ADLS and the performance tradeoffs of running distributed compute on top of them\n\nStrong programming skills in Scala, Java, Go, Rust, C++, or similar systems-oriented languages\n\nCuriosity about workload optimization, cost modeling, adaptive execution, and how compute efficiency affects AI and analytics at scale\n\nA pragmatic builder’s mindset: rigorous, hands-on, and comfortable owning complex systems end to end\n\nBonus\n\nExperience contributing to Apache Spark, Spark SQL, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems\n\nExperience with Catalyst, Adaptive Query Execution, cost-based optimization, query planning, vectorized execution, or distributed runtime systems\n\nExperience optimizing joins, aggregations, shuffles, scans, spills, caching, partitioning, skew handling, or task scheduling\n\nExperience building workload schedulers, execution control planes, resource managers, or multi-engine compute platforms\n\nExperience reducing compute cost or improving workload efficiency in large-scale production data environments\n\nBackground in query engines, distributed runtimes, storage-aware execution, indexing, caching, encoding, compression, or adaptive query optimization\n\nResearch or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure\n\nWhy Join Granica\n\nBuild foundational infrastructure for enterprise data and AI\n\nWork on deep systems problems across distributed compute, query execution, workload optimization, scheduling, resource management, and compute efficiency\n\nPartner directly with Product, Engineering, and company leadership\n\nHelp shape Crunch, Granica’s production data optimization platform for enterprise-scale lakehouse environments\n\nWork with a small, high-caliber team solving high-value infrastructure problems at massive scale\n\nHave direct influence on architecture, product direction, customer outcomes, and company growth\n\nCompensation & Benefits\n\nCompetitive salary, meaningful equity, and performance bonus for top performers\n\n401(k) with company match, comprehensive health coverage, and unlimited PTO\n\nDaily catered meals in our Mountain View office\n\nSupport for research, publication, and conference participation\n\nAt 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.\n\nCompensation Range: $160K - $240K","company":"Granica","rawCompany":"granica","city":"Alameda","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-31T11:32:53.109Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Software Engineer — Distributed Compute / Spark Systems","description":"Senior Software Engineer — Distributed Compute / Spark Systems\n\nLocation: Mountain View, CA — On-site\n\nAbout Granica\n\nGranica builds AI infrastructure for enterprises operating massive data environments.\n\nOur platform helps data and engineering teams reduce storage and compute costs, improve performance and reliability, and prepare large datasets for analytics and AI.\n\nGranica’s products include:\n\nCrunch — continuous optimization for enterprise lakehouse data\n\nMyelin — stateful infrastructure for long-running AI agents\n\nLarge Tabular Models — foundation models designed for enterprise tables\n\nTogether, we are building the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.\n\nGranica has demonstrated approximately $200K in annualized value per petabyte and verified customer value within weeks.\n\nAbout the Role\n\nGranica is hiring a Senior Software Engineer to build distributed compute systems for enterprise-scale data and AI workloads.\n\nYou 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.\n\nYou 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.\n\nThis 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.\n\nYou 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.\n\nWhat You’ll Do\n\nBuild distributed compute systems for large-scale analytical and AI workloads\n\nImprove performance and cost efficiency across Spark, Trino, Presto, Flink, Databricks, and Snowflake-adjacent environments\n\nDesign workload-aware systems for query execution, resource allocation, scheduling, and compute optimization\n\nOptimize execution performance across joins, aggregations, scans, shuffles, spills, caching, partitioning, and task scheduling\n\nBuild systems that learn from workload patterns and automatically improve execution plans, cluster usage, and compute efficiency\n\nDevelop infrastructure for adaptive workload routing, execution planning, and data-processing reliability across large customer environments\n\nDebug performance bottlenecks across query execution, metadata, storage, network, memory, CPU, and distributed compute layers\n\nWork with lakehouse tables and columnar formats such as Iceberg, Delta Lake, Hudi, Parquet, and ORC to improve end-to-end workload performance\n\nBuild systems that reduce compute waste caused by inefficient scans, poor partitioning, small files, skew, unnecessary shuffles, and suboptimal workload placement\n\nImprove reliability and failure recovery for large distributed data-processing jobs\n\nImplement algorithms in workload optimization, execution efficiency, cost modeling, and data-processing performance\n\nContribute to open-source or publish research when appropriate\n\nWhat We’re Looking For\n\nStrong engineering depth in distributed systems, data processing systems, query engines, databases, or cloud infrastructure\n\nProduction experience with distributed compute or query systems such as Apache Spark, Spark SQL, Trino, Presto, Flink, Databricks, EMR, Glue, Hive, or similar systems\n\nHands-on experience improving performance, reliability, or cost efficiency for large-scale data-processing workloads\n\nUnderstanding of distributed execution, query planning, scheduling, resource management, fault tolerance, and workload isolation\n\nExperience with Spark internals, Spark SQL, Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling\n\nFamiliarity with lakehouse formats and columnar data such as Iceberg, Delta Lake, Hudi, Parquet, or ORC\n\nFamiliarity with cloud object storage systems such as S3, GCS, or ADLS and the performance tradeoffs of running distributed compute on top of them\n\nStrong programming skills in Scala, Java, Go, Rust, C++, or similar systems-oriented languages\n\nCuriosity about workload optimization, cost modeling, adaptive execution, and how compute efficiency affects AI and analytics at scale\n\nA pragmatic builder’s mindset: rigorous, hands-on, and comfortable owning complex systems end to end\n\nBonus\n\nExperience contributing to Apache Spark, Spark SQL, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems\n\nExperience with Catalyst, Adaptive Query Execution, cost-based optimization, query planning, vectorized execution, or distributed runtime systems\n\nExperience optimizing joins, aggregations, shuffles, scans, spills, caching, partitioning, skew handling, or task scheduling\n\nExperience building workload schedulers, execution control planes, resource managers, or multi-engine compute platforms\n\nExperience reducing compute cost or improving workload efficiency in large-scale production data environments\n\nBackground in query engines, distributed runtimes, storage-aware execution, indexing, caching, encoding, compression, or adaptive query optimization\n\nResearch or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure\n\nWhy Join Granica\n\nBuild foundational infrastructure for enterprise data and AI\n\nWork on deep systems problems across distributed compute, query execution, workload optimization, scheduling, resource management, and compute efficiency\n\nPartner directly with Product, Engineering, and company leadership\n\nHelp shape Crunch, Granica’s production data optimization platform for enterprise-scale lakehouse environments\n\nWork with a small, high-caliber team solving high-value infrastructure problems at massive scale\n\nHave direct influence on architecture, product direction, customer outcomes, and company growth\n\nCompensation & Benefits\n\nCompetitive salary, meaningful equity, and performance bonus for top performers\n\n401(k) with company match, comprehensive health coverage, and unlimited PTO\n\nDaily catered meals in our Mountain View office\n\nSupport for research, publication, and conference participation\n\nAt 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.\n\nCompensation Range: $160K - $240K","datePosted":"2026-08-31T11:32:53.109Z","dateModified":"2026-08-31T11:32:53.109Z","hiringOrganization":{"@type":"Organization","name":"Granica","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Alameda","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f6a33862bdced8fd38cdd3d9"},"url":"https://jobsearcher.com/jobs/f6a33862bdced8fd38cdd3d9"}}