Staff Software Engineer - Cloud Platform

Calix

1d•Remote

About The Position

The Calix platform enables Communication Service Providers (CSPs) of all sizes to transform and future-proof their businesses. Through real-time data, automation, and actionable insights delivered via Calix One — our cloud-first, AI-powered platform — CSPs can simplify operations, collapse cost, and accelerate innovation. Calix One brings together the automation of everything and the experience of one, empowering customers to deliver differentiated subscriber experiences while driving acquisition, loyalty, and revenue growth. This is the Calix mission: to enable CSPs of all sizes to simplify, innovate, and grow, strengthening both their businesses and the communities they serve. We’re at the forefront of a once in a generational change in the broadband industry. Join us as we innovate, help our customers reach their potential, and connect underserved communities with unrivaled digital experiences. This is a remote based position in US. Please note that as part of the recruitment and hiring process, there is an in-person meeting that will take place. We, the Cloud Platform Engineering team at Calix are responsible for the Platforms, Tools, and CI/CD pipelines at Calix. Our mission is to enable Calix engineers to accelerate the delivery of world-class products while ensuring the high availability. We are seeking a skilled and experienced Staff Cloud Platform Engineer to join Cloud Platform team. The Staff Cloud Platform Engineer to design, implement, and manage cloud infrastructure and data pipelines using Google Cloud Platform (GCP) services like Kafka, Dataflow, and Dataproc. The ideal candidate will have a strong background in DevOps practices, cloud infrastructure automation, and big data technologies. You will collaborate with data engineers, developers, and operations teams to ensure seamless deployment, monitoring, and optimization of data solutions. This role also involves ensuring the smooth operation of Looker, supporting business intelligence (BI) initiatives, and enabling data-driven decision-making across the organization.

Requirements

10+ years of overall experience in DevOps cloud engineering, or data engineering.
5+ years of experience in DevOps, cloud engineering, or data engineering.
Proficiency in Google Cloud Platform (GCP) services, including Dataflow, Pub/Sub, Kafka, Dataproc, Big Query, and Cloud Storage.
Expertise in Infrastructure as Code (IaC) tools like Terraform or Cloud Deployment Manager.
Familiarity with data orchestration tools like Apache Airflow or Cloud Composer.
Strong proficiency in SQL query optimization
Experience with CI/CD tools like Jenkins, GitLab CI/CD, or Cloud Build.
Knowledge of containerization and orchestration tools like Docker and Kubernetes.
Strong scripting skills for automation (e.g., Bash, Python).
Experience with monitoring tools like Cloud Monitoring, Prometheus, and Grafana.
Familiarity with logging tools like Cloud Logging or ELK Stack.
Strong problem-solving and analytical skills.
Excellent communication and collaboration abilities.
Ability to work in a fast-paced, agile environment.

Responsibilities

Design and implement cloud infrastructure using IaC – Terraform
Automate provisioning and management of Dataproc clusters, Dataflow jobs, and other GCP resources
Integrate tools like GitLab CI/CD, or Cloud Build for automated testing and deployment.
Deploy and manage real-time and batch data pipelines using Dataflow.
Ensure seamless integration of data pipelines with other GCP services like Big Query, Cloud Storage, and Kafka or Pub/Sub.
Monitor performance, reliability, and cost of Dataproc clusters, Dataflow jobs, and streaming applications.
Optimize cloud infrastructure and data pipelines for performance, scalability, and cost-efficiency.
Implement security best practices for GCP resources, including IAM policies, encryption, and network security.
Ensure Observability is an integral part of the infrastructure platforms and provides adequate visibility about their health, utilization, and cost.
Collaborate extensively with cross functional teams to understand their requirements; educate them through documentation/trainings and improve the adoption of the platforms/tools.