Sr. Data Scientist

About Us

At Versa Networks, we're revolutionizing the way businesses connect, secure, and optimize their networks. Our mission is to secure anywhere, anytime access to anything. As a leader in Secure SD-WAN, SSE (Secure Service Edge), SASE (Secure Access Service Edge) and Next-generation Managed Services, we are empowering organizations across the globe to transform their IT infrastructure for the modern cloud era. Our innovative products enable enterprises to deliver a seamless, scalable, and secure digital experience, no matter where their users, devices, or applications are located. Founded by industry veterans and backed by premier venture capital firms, Versa is a market leader driving innovation and growth as it positions itself for a future IPO.

We believe in fostering a culture of innovation, collaboration, and customer success. Our team is comprised of passionate, forward-thinking professionals dedicated to driving the future of networking technology. We encourage creativity, offer opportunities for growth, and provide a dynamic environment where our people can thrive and make an impact.

At Versa Networks, we don't just build products - we build relationships, elevate businesses, and shape the digital future. Join us and be part of a fast-paced, cutting-edge company that's making a real difference in how the world connects and communicates.

Job Summary

We're seeking a highly skilled Data Engineer to design, build, and maintain production-grade data pipelines that process and transform terabytes of data. In this role, you'll collaborate closely with data scientists and other SWEs to ensure that our data infrastructure is scalable, reliable, and cost-effective.

Responsibilities

• Pipeline Development & Deployment:
• Architect, develop, and deploy batch and streaming pipelines using Airflow and containerized workflows for cyber-security use-cases.
• Containerize data-processing jobs with Docker, orchestrate with Kubernetes, and manage releases with Helm charts.
• Distributed Computing:
• Build high-throughput data transformations using Dask or Apache Spark.
• Maintain training data clusters across hybrid (on-prem and cloud environments).
• Optimize training jobs for performance, resiliency, and cost.
• Monitoring & Reliability:
• Implement observability (logging, metrics, alerting) to maintain pipeline health and SLA adherence.
• Troubleshoot, debug, and resolve data-processing failures in production.
• Collaboration & Best Practices:
• Work with cross-functional teams to define data contracts, schemas, and quality checks.
• Enforce software engineering best practices: CI/CD, code reviews, automated testing, and documentation.
• Data Modeling & Storage:
• Design and maintain data models and schemas for AI/ML continuous training use cases.
• Load data into cloud storage and lakes, ensuring performance and accessibility.