Engineering Manager – Foundational Data Systems for AI
Granica • San Francisco Bay Area, United StatesEngineering 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 substantial bonus for top performers
Flexible time off plus comprehensive health coverage for you and your family
Support for research, publication, and deep technical exploration
At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring. Join us to build the foundational data systems that power the future of enterprise AI!