Responsibilities :

- Promote a DataOps approach to Data science, engineering, and analytics delivery processes to automate the provision of data, testing, and monitoring, and shorten CI/CD.

- Collaborate with data & ML leads and create and build an optimal data pipeline architecture for the data solutions, including data science products.

- Ensure the data pipelines are scalable and performant, as well as create and maintain a service to connect data products.

- Create dashboards and other tools required to efficiently monitor our data and ML infrastructure, pipelines, ETL, and analytics delivery processes.

- Building an end-to-end event instrumentation and alerting system to detect and alert to any anomaly in the system or in the data.

- Assist in managing our data and ML infrastructure (upgrading, monitoring, optimising).

- Collaborate with IT DevOps engineers and participate in enterprise DevOps activities.

- Exchange your knowledge on infra and data standards with other developers and be part of our tech community. Promote the use of engineering best practices.

- Contribute to innovative POCs with our data & engineering teams.

- Remain flexible towards technology approaches to ensure that the best advantage is being taken by new technologies.

Requirements :

- Strong drive to solve problems, communicate clearly, and contribute positively to a DevOps/DataOps culture.

- Knowledge of the latest DevOps tools and practices.

- Experience with data pipelines within AWS (Glue, DataPipeline, Athena, EMR, DMS, Spark).

- Experience with Database Replication and databases like Aurora, MySQL, MariaDB, etc.

- Efficient in building CI/CD pipelines for containerized Java/Python codestack.

- Comfortable with Git workflow.

- Experience with applications deployed in AWS.

- Experience with configuration management and provisioning tools (e. g., Ansible, CloudFormation, Terraform).

- Knowledge of one or more scripting languages - Bash/Python/JavaScript.

- Orchestration/containerisation using Docker and Kubernetes.

- Basic knowledge of data science & ML engineering.

- Bachelor's Degree in computer science or a similar degree, or Big Data Background from top-tier universities.

- Experience: 8+ years of experience.