---
title: 'Senior Software Engineer — Lakehouse Systems at Granica'
canonical: 'https://feeny.ai/job/senior-software-engineer-lakehouse-systems-granica-san-francisco-4apk25mqqrb0'
type: 'job'
last_seen: '2026-10-20'
---

# Senior Software Engineer — Lakehouse Systems at Granica

- **Company:** Granica
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-06
- **Last confirmed live:** 2026-10-20
- **Apply:** https://jobs.ashbyhq.com/granica/dd24067b-7bb0-43f8-bb8f-ad679095078f

## Job description

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.

## About Granica

## Company Overview
- **One-liner**: Granica builds self-optimizing data infrastructure that compresses enterprise tabular data and enables structured intelligence for AI workloads.
- **Entity Type**: Private (Series A)
- **Headquarters**: Mountain View, California, United States
- **Founded**: 2023
- **Founders**: Not publicly available

## Core Business
- Primary industry/industries: AI Infrastructure, Data Compression, Research Services
- Target customers: Enterprise B2B — SaaS, consumer-internet, healthcare, and transportation companies with petabyte-scale data estates
- Mission or purpose statement: "Turning entropy to intelligence" — building a new class of data infrastructure that makes data estates efficient, reliable, and steerable for AI

## Products & Services
- **[Crunch]**: A self-optimizing, lossless compression layer for structured data (Iceberg, Delta, Trino, Spark, Snowflake, BigQuery, Databricks). Reduces storage by 45–80% and cuts cloud query spend by 15–35%. Deploys inside a customer's VPC with zero code changes and zero downtime. Continuously adapts to query patterns and data drift.
- **[EΣL (Extract, Signify, Load)]**: A reimagining of ETL. During "Signify," the system learns distributions, keys, and temporal drift while storing data, enabling real-time inference over a latent space without scanning cold blocks.
- **[Large Tabular Models]**: In-development systems that learn cross-column and relational structure to deliver trustworthy answers and automation with provenance and governance.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding — $45.0M (Series A, June 2023)
- **Lead Investor**: New Enterprise Associates (led the $45M Series A, with 6 total investors)
- **Notable Investors/Partners**: New Enterprise Associates, plus 5 other undisclosed institutional investors
- **Growth Signals**: 44.4% headcount growth year-over-year (35 employees), LinkedIn followers up 238.9% yearly, active 9 open job postings, deployments ranging from 1 PB to 100+ PB across dozens of enterprise customers

## Competitive Advantages
- **Entropy-aware compression**: Delivers state-of-the-art compression ratios (45–80% byte reduction) that are continuously adaptive to query patterns and data drift, unlike static compression schemes.
- **Zero disruption deployment**: Operates inside the customer's VPC with no code changes, no downtime, and day-zero activation — dashboards show savings before "coffee cools."
- **Research moat**: Foundational research published at NeurIPS 2024 (weighted empirical risk minimization with surrogate data) and ongoing work on statistical theory of data selection under weak supervision. Chief Scientist Andrea Montanari (Stanford) leads the research agenda.
- **Dual value proposition**: Simultaneously reduces storage costs (pennies per GB) and accelerates query latency (petabytes queried like terabytes), while also optimizing LLM token utilization by up to 50%.

## Strategic Focus
- **Near-term**: Scale Crunch adoption across enterprise data lakes, with a focus on Snowflake, Databricks, and BigQuery ecosystems.
- **Medium-term**: Expand from compression into advanced subsampling and safe synthetic data generation, turning any lake into a "self-optimizing data factory."
- **Long-term**: Build Large Tabular Models that enable real-time reasoning over exabyte-scale data without scanning cold blocks — replacing traditional warehouse scans with inferred answers.

## Why Work Here
- **High-impact engineering culture**: 51% of the team is in technical roles, 18% in research — the company is deeply engineering-first and research-driven. Engineers work on foundational data systems for AI at petabyte scale.
- **Cutting-edge ML research**: Opportunity to work alongside a Chief Scientist from Stanford and publish at top venues (NeurIPS 2024). The company is advancing the state-of-the-art in data compression, subsampling, and synthetic data.
- **Remote/hybrid/office policy**: Headquarters in Mountain View, CA (287 Castro Street). Job postings indicate Mountain View is onsite. Also has offices in India (9 employees) and Austria (1 employee).
- **Notable perks**: "Pays for itself" ROI philosophy — the product delivers measurable cost savings to customers. The company is well-funded ($45M Series A) with strong investor backing.
- **Team composition**: Small, high-leverage team (35 people) with alumni from Meta, Salesforce, Dremio, StackRox, UiPath, and Stanford. Alums go on to LangChain, Google, Rippling, Uber, and Temporal Technologies.
- **Active hiring**: 9 open positions including Senior Software Engineer (Foundational Data Systems), Engineering Manager, Research Scientist (Tabular & Structured ML), Staff Software Engineer, and Research Product Manager.

## Sources
1. [granica.ai](https://www.granica.ai/)
2. [granica.ai/about](https://www.granica.ai/about)
3. [LinkedIn](https://www.linkedin.com/company/granica-ai)
4. [PitchBook](https://pitchbook.com/profiles/company/528930-82)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/granica)

## Other roles at Granica

- [Research Scientist – Diffusion Models](https://feeny.ai/job/research-scientist-diffusion-models-granica-san-francisco-g1gcawhdzsns) — San Francisco, CA
- [Senior Software Engineer — Distributed Compute / Spark Systems](https://feeny.ai/job/senior-software-engineer-distributed-compute-spark-systems-granica-san-francisco-7k9r7jezn5t1) — San Francisco, CA
- [Software Engineer, Data Platform (Bengaluru)](https://feeny.ai/job/software-engineer-data-platform-bengaluru-granica-bengaluru-3s95wbaxc3y6) — Bengaluru, India
- [Forward Deployed Engineer](https://feeny.ai/job/forward-deployed-engineer-granica-san-francisco-3mqk9yj6gyez) — San Francisco, CA
- [Research Product Manager – AI Systems](https://feeny.ai/job/research-product-manager-ai-systems-granica-san-francisco-dmfh4stjx4qb) — San Francisco, CA
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- [Research Scientist – Large Tabular Models (LTMs)](https://feeny.ai/job/research-scientist-large-tabular-models-ltms-granica-san-francisco-2qa1k66qnecd) — San Francisco, CA
- [Head of Finance — Strategic Finance & Corporate Development](https://feeny.ai/job/head-of-finance-strategic-finance-corporate-development-granica-san-francisco-77nfgrymjsbe) — San Francisco, CA