Role Overview :

We are looking for a GCP DevOps / DataOps Engineer to design, automate, and maintain the cloud infrastructure.

In this role, you will work hand-in-hand with GCP Data Engineers, Analytics Consultants, and the client's central IT/platform teams to build automated, secure, and production-grade pipelines that ingest, clean, and model marketing and membership data across diverse acquisition and retention vehicles.

Key Responsibilities :

- Infrastructure as Code (IaC) : Design, provision, and maintain modular GCP cloud resources using Terraform, ensuring environment parity across Dev, Staging, and Production.

- DataOps CI/CD Pipelines : Build automated CI/CD pipelines (GitHub Actions / GitLab CI / Cloud Build) for data workflows, including BigQuery, Cloud Composer (Airflow), dbt, and Cloud Dataflow.

- Security & Compliance : Implement least-privilege IAM, service account management, customer data security standards, VPC service controls, and encryption (KMS) aligned with US retail data governance.

- Observability & Reliability : Establish end-to-end monitoring, automated alerting, and log routing across ingestion pipelines and analytical databases using Cloud Monitoring, Cloud Logging, and custom Slack/Teams notifications.

- Cost Optimization & Performance : Partner with Data Engineering to monitor query costs, storage tiers, auto-scaling thresholds, and resource utilization across GCP compute and analytics engines.

- Client & Stakeholder Collaboration : Coordinate with client platform engineering and infosec teams to navigate firewall rules, private connectivity, and compliance approvals.

Required Qualifications & Skills :

- Experience : 5+ years in DevOps/SRE, with at least 3+ years dedicated to Google Cloud Platform (GCP).

- IaC & Automation : Strong expertise in Terraform (writing reusable modules, state management in GCS, automated drift detection).

- Data & Analytics Stack : Practical experience operationalizing data stacks on GCP (BigQuery, Cloud Composer/Airflow, Cloud Storage, Pub/Sub, Dataflow/Dataproc).

- CI/CD Mastery : Deep hands-on experience building multi-stage deployment pipelines (GitHub Actions, GitLab CI, or Cloud Build) with automated testing, linting, and security scanning (SonarQube, Trivy, or Snyk).

- Scripting & Containers : Proficiency in Python and Bash; strong command of Docker containerization and container registry management (Artifact Registry).

- Networking & Security : Solid understanding of GCP VPCs, Private Google Access, Cloud NAT, IAM policies, and Service Accounts.

Preferred / Nice-to-Have :

- GCP Professional Cloud DevOps Engineer or Professional Google Cloud Architect certification.

- Experience in retail media, loyalty programs, or ad-tech data environments handling high-volume transaction/clickstream datasets.

- Exposure to MLOps tools (Vertex AI pipelines, feature stores, model registry).