Senior Software Engineer — Distributed Compute / Spark Systems

Granica
Bay Area Office, US
Hybrid

Who this role is best for

Candidates with distributed systems and Spark expertise will find this high-impact, on-site role in the Bay Area.

Best fit for

  • Individuals who have built or extended large-scale query engines or cloud infrastructure systems
    — “Production experience building, extending, or deeply optimizing distributed compute or query systems such as Spark, Spark SQL, Trino, Presto, Flink, or similar execution engines”
  • Candidates with a track record of solving complex performance and reliability challenges in distributed environments
    — “Debug bottlenecks across CPU, memory, network, storage, metadata, and distributed execution layers”

Things to consider

  • On-site presence is required in Mountain View, CA, with no mention of remote flexibility
    — “Location: Mountain View, CA — On-site”
  • The role requires ownership of core systems with direct impact on customer metrics
    — “Own core systems that directly affect customer compute spend, query latency, workload reliability, cluster efficiency”

How to stand out

  • Highlight experience with adaptive query execution and resource allocation in your resume and interviews
    — “Design adaptive systems for workload routing, execution planning, resource allocation, and cluster efficiency”
  • Showcase your work with Spark internals and related technologies like Delta Lake or Iceberg
    — “Hands-on experience with Spark internals such as Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling”
  • Demonstrate a builder mindset with end-to-end system development experience
    — “Strong programming skills in Scala, Java, Go, Rust, C++, or a similar systems-oriented language, with a pragmatic end-to-end builder mindset”
  • Include any contributions to open-source or research in distributed systems or query processing
    — “Research or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure”
Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build and optimize distributed compute infrastructure
  • improve execution performance
  • design adaptive systems for workload routing
Typical background
deep engineering experience in distributed systemsproduction experience building, extending, or deeply optimizing distributed compute or query systems

Skills & requirements

Required

Distributed SystemsSparkQuery EnginesResource ManagementFault TolerancePerformance Optimization

Preferred

Cost-based OptimizationVectorized ExecutionWorkload SchedulersExecution Control Planes

Stack & domain

Distributed SystemsQuery EnginesDatabasesCloud InfrastructureApache SparkTrinoPrestoFlinkIcebergDelta LakeHudiCloud Object StoresEnterprise-scale DataAI Workloads

About the role

Original posting from Granica via Ashby

SENIOR SOFTWARE ENGINEER — DISTRIBUTED COMPUTE / SPARK SYSTEMS

Location: Mountain View, CA — On-site

ABOUT GRANICA

Granica 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 include:

  • Crunch — continuous optimization for enterprise lakehouse data
  • Myelin — stateful infrastructure for long-running AI agents
  • Large Tabular Models — foundation models designed for enterprise tables

Together, 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 ROLE

Granica 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 systems role for engineers who have gone deep on distributed execution — particularly Apache Spark or similar query/runtime systems — and want to own performance, scheduling, reliability, and compute efficiency at massive scale.

WHAT YOU’LL DO

  • Build and optimize distributed compute infrastructure for large-scale analytical and AI workloads
  • Improve execution performance across scans, joins, aggregations, shuffles, spills, caching, partitioning, and task scheduling
  • Design adaptive systems for workload routing, execution planning, resource allocation, and cluster efficiency
  • Debug bottlenecks across CPU, memory, network, storage, metadata, and distributed execution layers
  • Improve reliability, fault tolerance, and compute efficiency across Spark and adjacent systems such as Trino, Presto, Flink, Iceberg, Delta Lake, Hudi, and cloud object stores

WHAT WE’RE LOOKING FOR

  • Deep engineering experience in distributed systems, query engines, databases, or cloud infrastructure
  • Production experience building, extending, or deeply optimizing distributed compute or query systems such as Spark, Spark SQL, Trino, Presto, Flink, or similar execution engines
  • Strong understanding of distributed execution, query planning, scheduling, resource management, fault tolerance, workload isolation, and performance optimization
  • Hands-on experience with Spark internals such as Catalyst, Adaptive Query Execution, shuffle, joins, aggregation, spill, memory management, or task scheduling
  • Strong programming skills in Scala, Java, Go, Rust, C++, or a similar systems-oriented language, with a pragmatic end-to-end builder mindset

BONUS

  • Contributions to Spark, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems
  • Experience with cost-based optimization, vectorized execution, workload schedulers, execution control planes, resource managers, storage-aware execution, or adaptive query optimization
  • Research or open-source contributions in distributed systems, databases, query processing, data processing, or cloud infrastructure

WHY JOIN GRANICA

  • Build foundational infrastructure for enterprise data and AI
  • Work on deep systems problems across distributed compute, query execution, scheduling, and compute efficiency
  • Own meaningful parts of the architecture in a small, high-caliber engineering team
  • Work directly with Product, Engineering, and company leadership
  • Have direct impact on customer performance, infrastructure cost, product direction, and company growth

COMPENSATION & BENEFITS

  • Competitive salary, meaningful equity, and performance bonus for top performers
  • 401(k) with company match, comprehensive health coverage, and unlimited PTO
  • Daily catered meals in our Mountain View office
  • Support for research, publication, and conference participation

At 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.

Source: Granica careers (Ashby)

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