Data Architect

A Data Architect at Great American Insurance Group is tasked with leading strategic architecture plans, optimizing data capabilities, and ensuring high-quality data management. You’ll guide teams in navigating data dependencies and managing risks while contributing to data project decisions aligned with our roadmap. Additionally, your expertise will shape our data governance framework and best practices, all aimed at delivering significant business value through our data strategies.

To be considered for the Data Architect position at Great American Insurance Group, candidates should have a bachelor’s degree in fields such as Engineering, Information Technology, or Computer Science. Experience as a data architect or engineer in building large-scale data solutions is essential, particularly with cloud-based platforms like Snowflake. Familiarity with data governance, engineering concepts, and data modeling is vital for success in this role.

Cloud experience is extremely important for the Data Architect role at Great American Insurance Group. Being skilled in architecting solutions on cloud-based platforms, especially Snowflake, is a significant aspect of the position. A strong foundation in cloud technologies allows the architect to effectively manage data modernization, migration, and warehousing, ensuring that our data capabilities meet modern enterprise needs.

Success for a Data Architect at Great American Insurance Group is measured by your ability to enhance data management practices that drive enterprise value. This includes establishing solid data governance frameworks, leading efficient, high-quality data initiatives, and fostering collaboration across projects. When data capabilities align seamlessly with business strategies and objectives, you’ll know you’re making a real impact.

Data Architects at Great American Insurance Group can look forward to competitive healthcare benefits that prioritize their wellbeing, along with retirement benefits to secure their future. Additionally, the company offers generous paid time off policies for both full-time and part-time employees, allowing for a healthy work-life balance.

When asked about your approach to designing data architecture, focus on the importance of aligning the architecture with business goals. Explain how you assess the current data landscape, engage with stakeholders, and utilize best practices in data modeling and management. Provide examples of frameworks you've implemented and how they have improved data quality and accessibility.

When discussing your experience with cloud-based data solutions, detail specific projects where you utilized platforms like Snowflake. Highlight your understanding of data migration, integration, and management in the cloud, and illustrate outcomes that demonstrate your ability to enhance data capabilities and drive business value.

In response to questions about data governance frameworks, mention specific methodologies you’ve applied, their goals, and outcomes. Discuss how you ensure compliance with data standards and quality controls, and underscore the importance of stakeholder collaboration in establishing effective governance practices.

Addressing data quality and integrity issues requires a proactive approach. Share your strategies for identifying these issues, including tools and metrics you utilize. Provide insights on how you've successfully resolved past issues and the impact your actions had on overall data reliability.

Collaboration is key in the role of a Data Architect. Discuss your experience working with cross-functional teams, how you facilitate communication between technical and non-technical stakeholders, and how collaboration has led to more effective data solutions. Provide examples of projects where teamwork directly contributed to success.

When assessing scalability, talk about your methods for evaluating current data systems to determine their capacity for growth. Share how you consider user demands, data volumes, and future needs, and provide examples of how you've designed scalable architectures in previous roles.

Familiarity with various data modeling techniques is essential. Discuss your experience with both conceptual and physical data models, and tell the interviewer about specific techniques such as entity-relationship modeling. Highlight how these methods have enhanced your ability to design efficient and effective data systems.

When sharing a data migration project, outline the project's scope, challenges faced, and your leadership role. Explain your strategy for ensuring a smooth transition, including planning, execution, and post-migration assessments. Emphasize the outcomes and how they added value to the organization.

When discussing challenges in data architecture, focus on specific experiences that tested your problem-solving skills. Sharing a story about a technical challenge or a stakeholder conflict can provide insight into your ability to navigate issues. Highlight the solutions you implemented and the lessons you learned.

In discussing your experience with data visualization tools, explain how you've utilized them to present data insights effectively. Mention specific tools you are proficient with, how they support your work in data architecture, and how they facilitate better business decision-making through clear data representation.