The ideal candidate has an emphasis on data infrastructure to help operationalize and maintain machine learning models and pipelines. The candidate will love automating via Infrastructure as code (IaC), be comfortable with AWS cloud services, and can design and implement end-to-end data and analytic infrastructure solutions.
As an offshore Senior DataOps Engineer , this candidate has the opportunity to innovate the way we do healthcare analytics and data. The candidate will be working closely with the Advanced Analytic and Data Engineering team to leverage the best technologies to establish a DataOps culture. Our goal is to enable fault tolerant, highly available, and accurate data ecosystem for advanced analytic projects.
Qualifications:
Minimum 3 years of qualified experience working with analytics, databases, and data systems
Have a strong knowledge of IaC automation principles
H ave expertise with Terraform and ideally a good knowledge of Ansible
Have experience with scripting and programming languages shell scripting (Not Mandatory) and Python (Required)
Have a good understanding of CI/CD and test automation
Have experience building docker containers, using version control and git in team-oriented environments
Cooperative and pragmatic with great communication skills
Enthusiast, keen to pick up new tools and technologies and work with emerging technology
Preferred:
Professional experience architecting/operating Data / DevOps solutions built on Azure
What are my responsibilities
Automate and enhance data lake ecosystem and machine learning pipelines using Terraform, CloudFormation/or CDK
Play a central role in a forming team to create easy ways of data access and ingest it into the data store in a reliable way
Build CI/CD pipelines for advanced analytic applications and operationalize predicative models along the (cloud) Data Engineer
Develop, deploy and operate dockerized data and model applications in Azure cloud-based environment.
Work closely with Infrastructure team and Security team, monitor data services (eg Azure Synapse Analytics) and ensure data governance and quality.