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Robinson Group has been retained by a very successful company to fill a permanent role that will shape the future of DataOps/DevOps & Analytics Operations within the company.

Prior experience for this role should be from a DevOps background, having built a DevOps environment that incorporates data architecture/data infrastructure – having also built out a DataOps environment with strategic partners (outside consulting partners)

Open to some working remotely – commutable to Cincinnati 2-3 times per week.

The team works with Data Science and engineering to identify what pipeline packages are important, and together build processes, tools, and technology to ensure that downstream consumers can trust and rely on data and insights.

This role will work with a diverse group of data scientists and engineers to broadly influence how the company trains, packages, investigates, and deploys data pipelines and machine learning models and other insight related artifacts. Will support the demand and interest of multiple teams to conduct research and experimentation, tool and platform development, packaging and deploying code and establishing DataOps best practices.

Responsibilities:

• Lead the operations and maintenance of reporting, machine learning, and data solutions.
• Work closely with Data Engineers and Data Scientists to troubleshoot and resolve data issues and maintain a high level of data accuracy in visualization and reporting.
• Review current state and identify improvement opportunities and best practice recommendations.
• Assists in troubleshooting and supporting complex platforms and data pipeline issues.
• Continue to innovate and build upon existing automation in our infrastructure and delivery capabilities.

Requirements:

• 10+ years of experience in DataOps(or DevOps) and data infrastructure
• 4-7 years of experience managing technical teams
• 5+ years of experience with AWS
• Expert in cloud-based data architectures
• Expert in Terraform and/or Cloud Formation
• Working and administration knowledge of Cognos, Tableau, Power BI or some other Business Insights system.
• Strong experience with software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
• Demonstrated experience with the mechanics of training and deploying ML models, as well as other aspects of the Data Science lifecycle, such as scheduling, CI/CD, cluster computing, server-less architectures, etc.

Director, AWS, DataOps, Data Operations, Cloud Formation, DevOps, Terraform