Description :

Roles & Responsibilities :

- Promote 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 optimal data pipeline architecture for the data solutions including data science products

- Ensure the data pipelines are scalable and performant as well as creating and maintaining 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 end-to-end event instrumentation and alerting system to detect and alert 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.

Required skills & Qualifications :

- 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 of 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 similar degree or Big Data Background from top tier universities