DevOps Engineer-II

Job description

Platform Engineering | Google Cloud | Data & AI Enablement We are looking for a Senior Developer to help build and evolve the CI/CD frameworks used by our Data and AI Platform teams. This role is focused on building reusable platform capabilities rather than one-off project-specific implementations. You will work closely with data engineers, AI/ML engineers, and platform teams to design reusable pipelines, automation frameworks, deployment patterns, and self-service engineering capabilities for modern data and AI workloads on Google Cloud Platform. The right person for this role is hands-on, comfortable writing production-quality code, and has a strong understanding of both cloud services and CI/CD engineering practices. You should be able to take complex platform requirements and turn them into scalable, reusable frameworks that engineering teams can easily adopt. Key Responsibilities Build Platform CI/CD Frameworks • Design and develop reusable GitLab CI/CD frameworks for Data and AI platform teams. • Create reusable pipeline templates, deployment workflows, validation steps, and automation standards that can be adopted across projects and environments. • Build self-service deployment capabilities and golden-path engineering workflows for Data & AI teams. Enable GCP Data Platform Deployments • Build deployment patterns and automation for core GCP data services including: • Dataproc • BigQuery • Dataform • Cloud Composer • Google Cloud Storage • Dataflow • Bigtable • Cloud Spanner • Enable teams to deploy, configure, and operate these services through reliable and repeatable CI/CD processes. • Design frameworks supporting multi-environment and multi-tenant deployment patterns. Support Data Pipeline Delivery • Design CI/CD workflows for: • Batch data pipelines • Streaming workloads • Transformation logic • Orchestration workflows • Environment-specific releases • Work with engineering teams to improve how data pipelines are built, tested, promoted, and deployed across environments. Develop Internal Tools and Automation • Build platform tools, APIs, CLIs, scripts, and reusable libraries using Python and Go. • Focus on reducing repetitive work, improving developer experience, and making platform capabilities easier for teams to adopt. • Apply software engineering best practices including testing, modular design, versioning, and maintainability to platform tooling and frameworks. Partner with Engineering Teams • Work closely with data, AI, platform, and infrastructure teams to understand engineering challenges and convert them into scalable platform solutions. • Help define practical standards for: • Pipeline design • Deployment automation • Release management • Operational readiness • CI/CD governance Must-Have Qualifications • Strong experience designing and building CI/CD frameworks for platform or engineering teams. • Expert-level knowledge of Google Cloud Platform and hands-on experience with GCP data services. • Deep experience with: • Dataproc • BigQuery • Dataform • Cloud Composer • GCS • Dataflow • Bigtable • Spanner • Strong experience with GitLab and GitLab CI/CD pipeline design. • Experience designing CI/CD pipelines for data platforms, analytics workloads, or AI/ML platform teams. • Strong understanding of data pipeline design including: • Batch processing • Streaming architectures • Orchestration • Transformation workflows • Environment promotion • Strong application development experience with Python. • Hands-on development experience with Go. • Ability to build reusable frameworks, deployment templates, automation utilities, and engineering standards. • Experience working with cross-functional engineering teams in platform, DevOps, or cloud engineering environments. Technical Skills Cloud Platform • Google Cloud Platform (GCP) GCP Data Services • Dataproc • BigQuery • Dataform • Cloud Composer • GCS • Dataflow • Bigtable • Spanner CI/CD & Automation • GitLab • GitLab CI/CD • Pipeline templates • Deployment automation • Environment promotion workflows Programming • Python • Go Data Engineering • Data pipeline design • Batch processing • Streaming workloads • Orchestration frameworks Platform Engineering • Reusable tooling • Automation frameworks • Developer enablement • Self-service platforms DevOps Practices • Version control • Release workflows • Automated validation • CI/CD governance