Senior Software Engineer — Lakehouse Systems at Granica (San Francisco, CA)Senior Software Engineer — Lakehouse Systems

Senior Software Engineer — Lakehouse Systems Location: Mountain View, CA — On-site

Granica is building the efficiency and intelligence layer for enterprise AI.

• Crunch makes massive enterprise data cheaper and easier to operate.
• Large Tabular Models learn from structured data to support shared intelligence across many capabilities.
• Myelin makes long-running AI agents more efficient and durable.

Granica has processed hundreds of petabytes of tabular data in production, and our research is led by Stanford Professor Andrea Montanari.

Granica is hiring a Senior Software Engineer to build foundational lakehouse systems for AI.

You will work on the core infrastructure behind Crunch, Granica’s continuous optimization product for enterprise lakehouse data. This includes systems for metadata management, transaction semantics, table maintenance, object-store-backed storage layouts, file optimization, and lakehouse cost/performance across petabyte- and exabyte-scale environments.

You will own core systems that directly affect customer infrastructure cost, query performance, table reliability, and the operational health of large lakehouse environments.

This is a hands-on systems role for engineers who have gone deep on lakehouse internals, table formats, metadata systems, storage layout, or distributed storage infrastructure.

• Build metadata, transaction, and table-maintenance systems for large-scale lakehouse datasets
• Work with Iceberg, Delta Lake, Hudi, manifests, snapshots, transaction logs, schema evolution, and garbage collection
• Optimize compaction, clustering, file sizing, data skipping, pruning, and physical data layout
• Improve performance and cost efficiency across Parquet/ORC and object stores such as S3, GCS, and ADLS
• Debug and optimize bottlenecks across metadata, storage, table maintenance, object-store access, and query execution

• Deep engineering experience in distributed systems, storage systems, databases, or data infrastructure
• Production experience building, extending, or deeply optimizing lakehouse or table-format systems such as Iceberg, Delta Lake, Hudi, or similar technologies
• Strong understanding of metadata architectures, transaction semantics, snapshots, manifests, schema evolution, and physical data layout
• Hands-on experience with table maintenance, compaction, clustering, file sizing, Parquet/ORC, and cloud object stores such as S3, GCS, or ADLS
• Strong programming skills in Java, Scala, Go, Rust, C++, or a similar systems-oriented language, with a pragmatic end-to-end builder mindset

Bonus

• Contributions to Iceberg, Delta Lake, Hudi, Parquet, ORC, Spark, Trino, Flink, Velox, DuckDB, DataFusion, or related systems
• Experience with small-file optimization, metadata scaling, delete handling, catalog consistency, indexing, caching, compression, or storage-engine internals
• Research or open-source contributions in distributed systems, databases, storage, compression, or data processing

• Build foundational infrastructure for enterprise data and AI
• Work on deep systems problems across lakehouse metadata, table formats, storage layout, and object-store behavior
• 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

• 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.