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Strong employer brand plus a common DevOps title increases applicant competition but role specialization mitigates it.
Deep GCP data-platform and CI/CD specialization limits easy transferability across unrelated industries.
Many mandatory technical must-haves (GCP data services, GitLab CI, Python, Go, platform CI/CD) increase screening rigor.
Design and develop reusable GitLab CI/CD frameworks and deployment automation for GCP Data and AI platforms.
Build self-service deployment capabilities and scalable platform tools for core GCP data services such as Dataproc, BigQuery, Dataform, Cloud Composer, Dataflow, Bigtable, and Spanner.
Collaborate with data, AI, platform, and infrastructure teams to deliver standardized pipeline, deployment, and release management frameworks across environments.
Strong experience designing and building CI/CD frameworks for platform or engineering teams.
Expert-level knowledge and hands-on experience with Google Cloud Platform and key data services: Dataproc, BigQuery, Dataform, Cloud Composer, GCS, Dataflow, Bigtable, Spanner.
Proficiency with GitLab CI/CD pipeline design and automation.
Strong programming skills in Python and hands-on experience with Go.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in creating reusable, scalable CI/CD frameworks rather than one-off solutions, supporting multi-environment and multi-tenant deployments.
Skilled in data pipeline design including batch processing, streaming architectures, orchestration, and environment promotions.
Practiced in partnering with cross-functional teams to translate complex platform requirements into operational standards and developer self-service capabilities on Google Cloud.