# Senior Software Engineer — Distributed Compute / Spark Systems

**Company:** [Granica](https://hotfix.jobs/companies/granica)
**Location:** Mountain View, CA
**Role:** Backend Engineering
**Salary:** $160k – $240k/yr
**Experience:** 5+ years
**Skills:** Spark, Spark Sql, Trino, Presto, Apache Flink, Databricks, Distributed Systems, Query Planning, Resource Management, Apache Iceberg, Delta Lake, Apache Hudi, Parquet, Orc, Scala
**Posted:** 2026-08-27

> Build and optimize distributed compute infrastructure for enterprise-scale analytics and AI workloads, improving query performance, reliability, scheduling, and compute costs. Requires senior-level distributed systems experience and production expertise with Spark or comparable query and data-processing engines.

## Job Description

## Responsibilities
- Build distributed compute systems for large-scale analytical and AI workloads.
- Improve performance, reliability, 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 joins, aggregations, scans, shuffles, spills, caching, partitioning, and task scheduling.
- Build systems that learn from workload patterns and improve execution plans, cluster usage, and compute efficiency.
- Develop adaptive workload routing, execution planning, and data-processing reliability infrastructure.
- Debug bottlenecks across query execution, metadata, storage, network, memory, CPU, and distributed compute layers.
- Improve performance of lakehouse tables and columnar formats including Iceberg, Delta Lake, Hudi, Parquet, and ORC.
- Reduce compute waste caused by inefficient scans, poor partitioning, small files, skew, unnecessary shuffles, and suboptimal workload placement.
- Improve reliability and failure recovery for distributed data-processing jobs.
- Implement algorithms for workload optimization, execution efficiency, cost modeling, and data-processing performance.
- Contribute to open-source projects or publish research when appropriate.

## Requirements
- 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 such as S3, GCS, or ADLS and the performance tradeoffs of distributed compute on these systems.
- Strong programming skills in Scala, Java, Go, Rust, C++, or similar systems-oriented languages.
- Curiosity about workload optimization, cost modeling, adaptive execution, and compute efficiency at scale.
- Pragmatic builder’s mindset with the ability to own complex systems end to end.

## Nice-to-haves
- Contributions to Apache Spark, Spark SQL, Trino, Presto, Flink, Velox, DuckDB, DataFusion, Iceberg, Delta Lake, Hudi, Parquet, ORC, or related systems.
- Experience with cost-based optimization, query planning, vectorized execution, or distributed runtime systems.
- 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.

## Compensation and Benefits
- $160,000–$240,000 annual salary.
- Meaningful equity and performance bonus.
- 401(k) with company match.
- Comprehensive health coverage.
- Unlimited PTO.
- Daily catered meals in the Mountain View office.
- Support for research, publication, and conference participation.

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