Skip to content
Granica

Senior Software Engineer — Lakehouse Systems

Bay Area OfficeOnsite · United States
Senior · FullTime · Engineering
POSTED 6 OCTVERIFIED 1D AGO
Apply Ask
Role
34 days opentrue age
Posted 6 Oct
Payats field
$160k–240kposted
▼ −2% vs market median $205k
$160k$240k
Market p25–p75
$182k–$229k
SF Bay Area · n=1042 · 365d to 6 Nov
CompanyGranica →
9open roles
Opened · 30d
+0
Closed · 30d
−0
30d window
Market
17,075open · Senior Software engineering
Opened · 7d
+9295% of open
All software engineering
75,198open
All tracked boards · live 10 Nov · n=17,075
GoJavaScalaRustC++SparkDistributed systems

Senior Software Engineer — Lakehouse Systems

Location: Mountain View, CA — Downtown HQ

Employment Type: Full-time

Work Model: On-site, 5 days/week

Department: Research

About Granica

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

Granica is building across three related layers:

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.

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

Source · GranicaFirst seen 4 NovOriginal ↗