Data Science Engineer

Data Science Platform Engineer

You will be part of Data Science Engineering Team which supports Enterprise Data Science Center and distributed Data Science Business Teams in the Organization. Our Team architects, implements and manages the cloud-based (AWS) and on-prem Data Science Platforms. As a Platform Engineer, you will be responsible for design and development of platform capabilities to deliver Data Science/ML at scale. The platform will be a combination of AWS services and custom components that together provide a complete solution that includes data pipelines, model development, model training, and production deployment of solutions while being compliant with enterprise policies.

Design and develop the platform capabilities, centered around AWS SageMaker, for self-service usage (by Data Scientists)

Lead the design and implementation of best governance practices for design, security, development, usability, cost control and forensics across cloud and on-prem infrastructure to support hybrid solutions

Design and automate ML deployment pipelines (MLOps) to define a clear path to production for Data Science Projects

Provide L3 engineering support and lead the delivery of infrastructure solutions on the platform.

Minimum Qualifications:

Ideal candidate should have 5+ years of prior experience in IT infrastructure; network and server engineering, data center in a high-tech environment.

3+ years successfully designing and implementing successful public cloud solutions (AWS).

Proven work experience as a Platform Engineer or similar role in a cloud native environment.

Experience in deploying and implementing scalable cloud-based solution architectures for PaaS, IaaS or SaaS.

Deep hands-on experience leading the design and deployment of technology infrastructure, network, compute, storage, and virtualization.

Experience with cloud analytics services and data engineering is a plus.

Skills:

Good understanding of Data science model lifecycle stages

Proficiency in Python development

AWS cloud Experience - especially in Data and Analytics Space - Athena, Redshift, Glue, SageMaker

AWS cloud formation/deployment - Infrastructure as Code

GitLab and AWS CI/CD enabling tool stack. GitOps is mandatory.

AWS SageMaker Experience - Build, Train, Test, Deploy.