Granica is hiring an Engineering Manager – Foundational Data Systems for AI

Engineering Manager — Foundational Data Systems for AI Location: Downtown Mountain View, CA (office-based, 5 days/week) Team: Foundational Data Systems About the Role We’re hiring an Engineering Manager (Hands-on TLM) to lead Granica’s Foundational Data Systems—the core infrastructure layer that everything else depends on. You’ll lead a globally distributed team of ~15–20 senior engineers across the US, India, and Canada, with direct ownership of long-lived systems spanning storage, metadata, and distributed compute. This is not a people-only management role. It is a hands-on technical leadership position requiring regular participation in architecture reviews, system design, debugging complex distributed systems issues, and occasional coding. You will help design and operate the core infrastructure behind Granica’s open-table data systems, working with formats such as Apache Iceberg, Delta Lake, and Parquet. Deep experience with open table formats and their internals—including metadata layers, transaction commits, compaction, schema evolution, and table maintenance—is strongly preferred. The systems this team builds determine whether Granica can translate cutting-edge research into reliable products and whether our customers can trust the data foundations their analytics and AI workloads depend on. This role is ideal for a technically strong leader who enjoys building teams, designing durable distributed systems, and operating infrastructure that must be correct and efficient at enterprise scale. Why This Role Exists Modern AI and analytics systems are constrained less by models and more by the inefficiency and fragility of the data infrastructure beneath them. Redundant data layouts, brittle metadata layers, and expensive execution paths translate directly into: - higher infrastructure costs - slower iteration cycles - operational fragility Granica’s mission is to remove that inefficiency at the foundation. We design self-optimizing data systems that continuously reorganize, compress, and maintain structured data so it remains efficient, reliable, and reversible as it evolves. These systems serve as the durable substrate for analytics, automation, and AI workloads—where correctness, provenance, and governance are non-negotiable. This team works closely with Granica’s Research group led by Prof. Andrea Montanari (Stanford), translating advances in information theory and learning efficiency into production-grade distributed systems. We believe the next major step forward will come not from larger models, but from better systems and better data. What You’ll Own Team Leadership & Growth - Lead, mentor, and grow a senior engineering team across multiple geographies - Own hiring, onboarding, and career development in a high-bar engineering culture - Create clarity and accountability across a large, senior organization operating in ambiguity Technical Direction & Architecture Set technical direction through design reviews, RFCs, and principled architectural trade-offs. Remain deeply involved in architecture and system design decisions. Own the evolution of foundational systems including: - Table maintenance and data layout optimization - Metadata services and transaction management - Schema evolution and versioning - Distributed compute orchestration - Reliability, observability, and operational tooling These systems operate on petabyte-scale datasets and must meet strict requirements for correctness, durability, and efficiency. Execution & Operational Excellence - Translate strategy into execution through clear roadmaps and milestones - Establish engineering standards for correctness, reliability, and cost efficiency - Lead incident response, postmortems, and system improvements - Operate infrastructure where correctness and durability are critical Cross-Functional Partnership - Work closely with Research, Applied Systems, Product, and Infrastructure teams - Help move ideas from theory to production while preserving system integrity - Act as a technical and organizational bridge between research and engineering What You Bring Minimum Qualifications - 7+ years of experience in backend, infrastructure, or distributed systems engineering - 2+ years leading engineering teams or large multi-person technical initiatives - Strong system design experience across distributed compute, storage, or data platforms - Experience building or operating large-scale data infrastructure systems Hands-on experience with one or more open table formats such as: - Apache Iceberg - Delta Lake - Apache Hudi - or similar table-layer technologies Understanding of table format internals, such as: - metadata layers and manifests - snapshot / transaction commit models - file compaction and table optimization - schema evolution and partitioning strategies Experience working with distributed compute frameworks such as: - Spark - Trino / Presto - Flink - Ray Strong programming ability in Go, Java, Scala, or Python Comfort operating as a hands-on engineering leader (~30–40% technical) while managing a team. Preferred Qualifications - Experience building data lakehouse platforms - Experience working on table format implementations or metadata services - Experience operating systems at petabyte-scale data volumes - Experience partnering closely with research or ML infrastructure teams - Experience scaling engineering teams in fast-moving, high-ambiguity environments Logistics Location: Downtown Mountain View, CA Work model: Office-based, five days per week Team size: ~15–20 engineers across the US & India 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.