Data Warehouse Engineer

As a Data Warehouse Engineer at IntelliShift, your primary responsibilities will include designing, building, and maintaining a multi-terabyte data warehouse to facilitate analytical insights. You will participate in project planning, manage support tickets, monitor data quality, and design development of ELT/ETL pipelines. Additionally, you'll craft reports and stored functions, ensuring that the entire data ecosystem operates seamlessly.

To be considered for the Data Warehouse Engineer position at IntelliShift, you need a bachelor’s degree in computer science or a related field, along with 2+ years of experience with Snowflake Data Warehouse or similar technology. A solid background in SQL and stored procedure/function development for at least 4 years is essential. You should also demonstrate knowledge of data warehousing concepts and the ability to develop analytic queries.

IntelliShift values candidates who have experience with data replication concepts, as well as skills in ETL/ELT procedure development. Familiarity with JavaScript and machine learning is also beneficial. However, a strong commitment to research and resolving data issues will set you apart as the ideal Data Warehouse Engineer.

At IntelliShift, we promote a culture of growth where employees are encouraged to stay updated with developments in data warehousing and big data. Our core values focus on thinking bigger and embracing change, which are ingrained in our career development programs, ensuring you have the resources and support to grow both professionally and personally.

The compensation package for a Data Warehouse Engineer at IntelliShift ranges from $130,000 to $140,000, commensurate with experience. In addition to competitive pay, we offer a comprehensive benefits package that includes company-subsidized medical insurance, dental, vision, and a 401K plan with a 4% company match, reflecting our appreciation for our employees' contributions.

In answering this question, emphasize your specific projects and responsibilities while using Snowflake. Discuss the architecture you implemented, any challenges you faced, and how you overcame them. Highlight your understanding of its features and how they contributed to the success of your projects.

When discussing data quality monitoring, illustrate your systematic approach. You might mention setting up automated data validation processes, regular audits, and using metrics to evaluate data accuracy. Share examples of how you've identified and resolved data quality issues in past roles.

For this question, outline your experience defining, designing, and implementing both ETL and ELT processes. Discuss the technologies you've used, the data sources you've worked with, and how these processes improved data accessibility for end-users in your previous roles.

In addressing this question, display your analytical problem-solving skills by discussing the techniques you employ to quickly investigate and resolve issues. Talk about your method for prioritizing tickets based on impact, communication with stakeholders, and steps taken to prevent future occurrences.

Here, provide a specific example of a complex report that required innovative thinking. Discuss the requirements, the data sources you utilized, the challenges faced in trying to actualize the report, and the positive feedback received from stakeholders after the report was delivered.

For this question, demonstrate your ongoing commitment to professional growth by discussing the resources you utilize like online courses, webinars, and industry publications. Mention any relevant communities or events you participate in to exchange knowledge and stay updated on trends.

In your response, stress the importance of collaboration and how you actively engage with cross-functional teams to align data warehouse design with business objectives. Mention specific instances where team collaboration led to improved outcomes.

Describe the various strategies you implement for optimization, such as ensuring optimal indexing, utilizing partitioning, and minimizing data retrieval. Providing a brief example of query optimization that resulted in performance improvements will strengthen your answer.

Discuss the measures you take to secure sensitive data, such as implementing access controls, encryption methods, and compliance with regulations like GDPR. Emphasize your proactivity in staying informed about data security best practices.

In answering this question, highlight your approach to assessing business needs, scalability, data integration, and ensuring overall performance. Discuss areas such as data modeling techniques and your experience with architectural frameworks.