Role Overview :

We are looking for a highly skilled and motivated DevOps Lead to design, build, and operate scalable, secure, and highly available cloud and data platforms, primarily on Google Cloud Platform (GCP) with Amazon Web Services (AWS) as a secondary environment.

This is a hybrid platform role. Alongside core DevOps ownership - Terraform, Kubernetes, CI/CD, cloud security, and observability - you will also contribute substantially to our data engineering function: building and operating production Python data pipelines, orchestrating batch and streaming workflows, and owning the infrastructure that sits underneath our data platform.

DevOps & Cloud Infrastructure Responsibilities :

- Design, implement, and manage cloud infrastructure across GCP and AWS using Terraform and other Infrastructure-as-Code practices.

- Deploy and maintain Kubernetes clusters and manage application workloads using Helm and/or ArgoCD.

- Build and enhance secure, scalable CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI, and ArgoCD.

- Implement best practices in containerization using Docker, including image hardening and container runtime security.

- Integrate quality gates in CI/CD, including automated testing, code coverage, and SonarQube configuration.

- Ensure secure CI/CD processes, including secrets management, least-privilege access, and hardened build agents.

- Implement and maintain cloud security best practices: IAM roles and policies, network segmentation, encryption, and vulnerability management.

- Collaborate with security teams to embed DevSecOps practices.

- Contribute to system architecture, monitoring, and observability using modern tools and practices.

- Participate in incident response, post-incident reviews, and root cause analysis.

Data Engineering & Data Platform Responsibilities :

- Design, build, and maintain production-grade ETL/ELT pipelines in Python for both batch and streaming workloads.

- Build and operate workflow orchestration using Airflow / Cloud Composer.

- Develop and optimise data models and transformations in BigQuery (and equivalents such as Snowflake or Redshift).

- Own the infrastructure and deployment lifecycle of data services - provisioning, autoscaling, upgrades, and cost management.

- Implement data quality, validation, lineage, and observability checks.

- Apply software engineering discipline to data workloads: version control, code review, automated testing, and CI/CD for pipelines and transformations.

- Work with analytics, ML, and product teams to make data reliably available, secure, and well governed.

Must-Have Skills :

- 8+ years of total professional experience in software, data, or infrastructure engineering.

- Minimum 4 years in a dedicated DevOps / SRE / Platform Engineering role.

- Substantial prior hands-on data engineering experience at a senior level.

- Strong proficiency in Google Cloud Platform (GCP) and solid working experience with AWS.

- Hands-on experience with Terraform, Kubernetes, Docker, and Helm.

- Expertise with CI/CD tools: Jenkins, GitLab CI, GitHub Actions, ArgoCD.

- Strong production-grade Python and solid Bash.

- Advanced SQL and hands-on experience with a cloud data warehouse (BigQuery preferred).

- Experience with workflow orchestration (Airflow) and distributed processing frameworks.

- Experience with streaming or event-driven data (Pub/Sub, Kafka, or Kinesis).

Soft Skills :

- Strong problem-solving abilities.

- Ability to mentor junior engineers across both infrastructure and data disciplines.

- Confidence in presenting technical concepts to diverse stakeholders.

- Comfortable operating across two domains and prioritising between them.