Senior Data Warehouse Engineer

As a Senior Data Warehouse Engineer at Shearer's, your responsibilities will primarily include designing, developing, and maintaining our data warehouse solutions. This involves creating efficient data architectures and models, implementing Power BI dashboards and reports for insightful analytics, managing ETL processes, and ensuring data quality and performance optimization. Collaboration with cross-functional teams will also be vital to understand their data needs and deliver effective solutions.

To qualify for the Senior Data Warehouse Engineer role at Shearer's, you should have a Bachelor's or Master's degree in Computer Science, Information Technology, or a related field, along with 5-10 years of progressive experience in data modeling and engineering. Proficiency in SQL, Power BI, and data warehousing concepts is essential, while familiarity with Azure-based solutions and best practices is preferred.

At Shearer's, Power BI plays a crucial role in our business intelligence strategy. As a Senior Data Warehouse Engineer, you'll be responsible for developing and implementing Power BI semantic models, dashboards, and reports that provide actionable insights to various stakeholders, helping them make informed decisions based on data.

Preferred skills for the Senior Data Warehouse Engineer position at Shearer's include proficiency in SQL Server programming, experience with Azure and cloud-based data warehousing solutions, familiarity with SSIS, SSAS, and integration software like Boomi, as well as knowledge of data governance best practices and exposure to AI/ML analytics are all beneficial.

When you join Shearer's as a Senior Data Warehouse Engineer, you'll enjoy a comprehensive benefits package that includes medical, dental, vision, and life insurance. Additionally, you’ll have access to a flexible spending account, retirement savings plans with matching contributions, and various disability insurances. Plus, we offer opportunities to volunteer in the communities we operate in!

In responding to this question, focus on specific projects you've worked on that involved data warehouse design and implementation. Highlight your role, the technologies used, and any challenges you faced, along with how you overcame them. Mentioning how your designs facilitated business intelligence and analytics can also make your answer more compelling.

Discuss the methods you utilize to ensure data quality, like regular data cleansing, and validation processes. Explain how performance optimization techniques, such as indexing and efficient ETL processes, contribute to the overall success of the data warehouse. Real-world examples of these practices in past roles will make your answer stronger.

Share your approach to designing and managing ETL processes, including tools and methodologies you often use. Emphasize the importance of effective data flow and integration, as well as your experiences in troubleshooting and optimizing ETL pipelines for better performance.

Detail your experience with Power BI, including how you've developed semantic models, dashboards, and reports. Emphasize your understanding of DAX and Power Query, and provide examples of how your use of Power BI has driven insights and improved decision-making processes in your previous roles.

Discuss your knowledge of data governance principles and best practices, including data accuracy, security, and compliance. Emphasize how data governance frameworks facilitate collaboration and ensure data is managed properly within an organization, particularly in large data projects where multiple stakeholders are involved.

Reflect on specific challenges related to data quality, integration, or project deadlines. Describe the steps you took to resolve these issues, including collaborating with team members, implementing new tools or processes, and any lessons learned that you applied in future projects.

Mention your approach to staying updated with new trends, such as attending conferences, participating in online courses, or following industry thought leaders and publications. Illustrate how keeping abreast of these trends has benefitted your work and provided innovative solutions in past projects.

Use the STAR method (Situation, Task, Action, Result) to discuss a specific instance of collaboration with a cross-functional team. Highlight your contributions, the project objectives, and how effective communication and teamwork led to a successful outcome.

Discuss your hands-on experience with cloud platforms like Azure, AWS, or Google Cloud in relation to data warehousing. Provide insights into specific projects where you migrated on-premises solutions to the cloud, and the benefits and challenges associated with such transitions.

List relevant tools and technologies you are proficient in, such as SQL, Power BI, Azure, and any ETL tools you have experience with. Provide concrete examples of how these tools have been utilized to solve problems or enhance data workflows in past roles.