Cloud Solution Architect - Platform Operations Lead

Job Description

This role is based remotely but if you live within a 50-mile radius of Warren, MI, you are expected to report to that location three times a week, at minimum

The Role:

General Motors is a forward-thinking organization dedicated to harnessing the power of data to drive business growth and innovation. We are looking for an accomplished Lead Architect with a strong background in creating data assets, developing data products, and a robust release management practice that allows GM to implement machine learning models. As a pivotal member of our technology leadership team, you will play a key role in shaping our data architecture strategy and ensuring the successful delivery of insights that contribute to the organization's financial success.

This role focuses on the day to day Operations of the GM "Insight Factory" - including End to End health of the data ecosystem.

What You'll Do:    
 

Data Architecture and Strategy: 

• Lead the development and execution of the overall data architecture strategy. (Azure based with Databricks at this time)
• Design and implement robust data assets, ensuring scalability, reliability, and performance.
• Add functionality/expand the limits of functionality of GitHub Enterprise, GitHub Actions, Git Flow (including CoPilot)
• Build additional functionality into the existing CICD to produce reliable release artifacts that allow us to control the quality of code in our environment.
• Work closely with the Operations Lead to implement APM that allows for the creation and monitoring of “End to End” timing charts – every step and no step missed.
• Work with Infra Leads to implement deployment isolation and release best practices to ensure smooth, frequent releases with appropriate rollback.

Data Product and ML Model and Application Development: 

• Oversee the creation of data products that leverage the organization's data assets. 
• Lead the development and deployment of machine learning models that enhance decision-making processes
• Leverage our existing stack – including ML Flow - to implement ML Ops and create a model registry.
• Develop approach to support Dev,Test,PreProd and Prod environment release process.

Integration with Dashboards and Applications: 

• Collaborate with cross-functional teams to integrate data assets, data products, and ML models into dashboards and applications. 
• Ensure seamless interaction between data components and front-end applications. 

Insight Generation and Monetization: 

• Play a pivotal role in generating actionable insights from data products and ML models. 
• Work closely with business stakeholders to identify opportunities for monetizing data assets and insights. 

Hands-on Leadership: 

• Be hands-on with the …