Data Engineer

Zero Networks is a fast-growing cybersecurity startup redefining how organizations protect themselves. Our Data Team is at the heart of that mission - powering everything from security insights to customer-facing intelligence.

Join us as a Data Engineer to help develop and maintain our rapidly growing data platform. You will be responsible for implementing scalable data pipelines, contributing to the development of custom tools, and ensuring massive datasets are processed efficiently to drive critical business decisions.

Requirements:

BS or MS in Computer Science or a related technical field.
3+ years of experience as a Software Developer or Data Engineer.
3+ years of hands-on Python development experience as part of an engineering team.
Hands-on experience building and deploying production-grade data pipelines in cloud environments (GCP preferred), with full lifecycle ownership - from development to production.
Hands-on experience developing, testing, and deploying production-grade applications - from coding and reviews to CI/CD, containerization, and production operations.
Hands-on experience with Kubernetes, including deploying and managing applications with Helm and configuring production-ready environments.
Familiariry with modern data lake and data warehouse concepts, including the separation of compute, storage, and metadata layers. Hands-on experience with Trino + Iceberg or similar architectures is a strong advantage.

Responsibilities:

Implement and maintain scalable, production-grade data pipelines and infrastructure that support analytics and new product features.
Build and contribute to internal software and tooling that improves data team workflows, focusing on driving structure, maintainability, and engineering best practices.
Develop and manage key components of our new data platform, ensuring high reliability, performance, and scalability.
Work closely with security researchers, data analysts, and product teams to translate innovative cybersecurity ideas into functional, production-ready data solutions.
Apply and support best practices in data quality, governance, and observability to ensure our data systems remain robust and trustworthy as we scale.