Senior Software Engineer — Lakehouse Systems

LOCATION: MOUNTAIN VIEW, CA — DOWNTOWN HQ

Employment Type: Full-time

Work Model: On-site, 5 days/week

Department: Research

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

• 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

• Crunch reduces storage and processing costs across enterprise data infrastructure.
• Large Tabular Models unlock predictions and generated data from governed enterprise tables.
• Myelin reduces token and compute overhead for long-running AI agents.

Everything runs inside the customer’s perimeter, so enterprises retain control of their data and the intelligence it creates.

Granica has already processed hundreds of petabytes of enterprise data in production for customers including Snap, Pinterest, Lyft, and ShareChat.

Our research organization is led by Stanford Professor Andrea Montanari.

We operate at the intersection of data infrastructure, AI systems, and applied research, building the efficiency and intelligence layer for enterprise AI.

• 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.

About Granica

Granica helps enterprises run AI for less, from the data layer to agent execution.

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