Software Engineer, Infrastructure

Join Granica’s core engineering team to build and scale the infrastructure powering massive data systems, automated deployments, and high‑reliability AI workloads. This is a deep engineering role — not ops, not feature delivery. The infrastructure you build directly enables:

• exabyte‑scale data systems
• fast research‑to‑production cycles
• reliable enterprise AI workloads

This role offers deep ownership, high technical impact, and a long horizon.

What You’ll Do

• Design and operate cloud infrastructure supporting petabyte–exabyte‑scale systems.
• Manage and evolve production Kubernetes clusters with high availability and predictable scaling.
• Build and optimize CI/CD pipelines for faster builds, reliable tests, and safer deployments.
• Improve developer experience by reducing friction and automating workflows.
• Contribute to E2E testing and release‑confidence systems.
• Strengthen observability across logging, metrics, tracing, and alerting.
• Collaborate across engineering teams on architecture, reliability, and scaling challenges.
• Own customer‑facing technical integration — guide deployments, troubleshoot infra‑level issues in customer environments, and ensure Granica runs reliably within diverse cloud, data, and security stacks.
• Drive operational excellence through reliability, automation, and best practices.

What We're Looking For

• 5+ years in infrastructure, platform, or distributed systems engineering
• Strong coding skills in Go , Java , Python , or similar
• Production‑grade Kubernetes experience with strong cloud and containerization skills across AWS / GCP / Azure and Docker .
• Deep experience with CI/CD systems and cloud infrastructure
• Proficiency with infrastructure‑as‑code ( Terraform )
• Ability to debug complex, cross‑layer issues (networking, storage, runtime)
• Track record of designing scalable, reliable, cost‑efficient systems
• Thrives in fast‑paced startup environments with strong communication and ownership.

Nice-to-Haves

• Experience with data platforms or lakehouse systems
• Familiarity with Spark , Trino , Iceberg , Delta , Airflow
• Exposure to observability stacks ( Prometheus , Grafana , ELK , Datadog )