Sr. Data Scientist

Versa Networks•Santa Clara, CA

21h•$150,000 - $220,000•Remote

About The Position

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.

Requirements

3–5 years of professional experience designing and operating production data pipelines at scale.
Expertise with Docker, Kubernetes, and Helm.
Hands-on experience building DAG-based pipelines in Apache Airflow.
Strong proficiency in Python for data engineering tasks.
Practical experience with Dask or Apache Spark for large-scale data processing.
Familiarity with deploying and managing services in a cloud environment.
Experience writing data services in Go or Rust.
Hands-on with Google Cloud services (e.g., Pub/Sub, Big Query, Cloud Storage, GKE). Equivalent experience in other public cloud providers is fine.
Exposure to deploying cross-cluster model-training workflows using Ray or similar frameworks.
Familiarity with Terraform for deployment.
Knowledge of data governance, encryption, and role-based access control.

Responsibilities

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.
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.
Implement observability (logging, metrics, alerting) to maintain pipeline health and SLA adherence.
Troubleshoot, debug, and resolve data-processing failures in production.
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.
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.