> Markdown version of [/jobs/ext/1459903-sr-data-scientist](https://www.wearedevelopers.com/jobs/ext/1459903-sr-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Scientist - **Company:** Versa Networks - **Location:** Santa Clara, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $150,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Airflow, Automation of Tests, Big Data, BigQuery, Cloud Computing, Cloud Storage, Code Review, Computer Programming, Continuous Integration, Data as a Services, Information Engineering, Data Governance, Data Infrastructure, Software Debugging, Distributed Systems, Python (Programming Language), Operational Databases, Role-Based Access Control, Data Logging, Data Processing, Google Cloud, Apache Spark, Kubernetes Helm Charts, Containerization, Kubernetes, Dask, Machine Learning Operations, Terraform, Data Pipelines, Docker - **Published:** July 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f6cd7a40acf5c8ea ## About the Role * 3-5 years of professional experience designing and operating production data pipelines at scale. * Containerization & Orchestration: Expertise with Docker, Kubernetes, and Helm. * Workflow Management: Hands-on experience building DAG-based pipelines in Apache Airflow. * Programming: Strong proficiency in Python for data engineering tasks. * Distributed Frameworks: Practical experience with Dask or Apache Spark for large-scale data processing. * Cloud Fundamentals: Familiarity with deploying and managing services in a cloud environment. * Compiled Languages: Experience writing data services in Go or Rust. * GCP Proficiency: Hands-on with Google Cloud services (e.g., Pub/Sub, Big Query, Cloud Storage, GKE). Equivalent experience in other public cloud providers is fine. * ML Pipelines: Exposure to deploying cross-cluster model-training workflows using Ray or similar frameworks. * Infrastructure as Code: Familiarity with Terraform for deployment. * Security & Compliance: Knowledge of data governance, encryption, and role-based access control. Location: United States * Applicants must be authorized to work in the US The pay range for this position at commencement of employment in California, Washington, or New York City is expected in the range of $150,000 to $220,000. A candidate's specific pay within this range will depend on a variety of factors, including job-related skills, training, location, experience, relevant education, certifications, and other business and organizational needs. ## Description 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How Much Does a Software Engineer Make? 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