Senior Data DevOps
As a Senior Data DevOps you will be responsible for designing, implementing, and maintaining the infrastructure and deployment pipelines for our analytics solutions in the cloud.
Reporting to the Senior Data Engineering Manager, you will be part of the Data Operations Team. This position requires a strong background in cloud technologies, analytics platforms and DevOps practices.
Main duties:
- Design, implement, and manage cloud infrastructure for analytics solutions
- Ensure scalability, reliability, and performance of cloud-based analytics platforms solutions, with strong proactive focus on cost optimistation
- Develop and maintain automated deployment pipelines for analytics applications
- Implement continuous integration and continuous delivery (CI/CD) practices.
- Collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to ensure seamless integration of analytics solutions.
- Implement monitoring and logging solutions to proactively identify and address issues.
- Optimize cloud resources for cost efficiency and performance.
- Implement security best practices for analytics solutions in the cloud.
- Ensure compliance with relevant data protection and privacy regulations.
- Maintain comprehensive documentation for infrastructure, configurations, and processes.
- Provide support to other team members, including code reviews, knowledge sharing and mentoring, while fostering a continuous learning culture
- Support the “think first” approach and design process
Essential Requirements:
Essential:
- Proven experience as a DevOps Engineer or similar role, with a focus on analytics solutions.
- End-to-end SaaS integration
- PaaS, IaC (Terraform, AWS CloudFormation)
- Logging, monitoring, and alerting all aspects of the platform
- Cloud and data security experience (encryption/tokenisation)
- Proficiency in AWS
- Automation and scripting skills (e.g., Python, Shell scripting).
- Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
- CI/CD pipelines and tools (e.g. DBT, Jenkins, GitLab CI)
Desirable:
- Experience with analytics tools and frameworks (e.g., Apache Spark, Hadoop).
- SQL
- Sagemaker, DataRobot
- Google Cloud and Azure
- Data platform metadata driven frameworks to ingest, transform and manage data