Senior Software Engineer — Lakehouse Systems
Granica · San Francisco
Senior Software Engineer — Lakehouse Systems
Location: Mountain View, CA — On-site
About Granica
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.
About the Role
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.
What You’ll Do
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
What We’re Looking For
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
Why Join Granica
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
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.
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