Okay, I've searched for Senior Data Engineer roles in Plano, TX, specifically focusing on expertise in Snowflake and DBT. These roles are contract positions requiring at least 6 years of experience with strong SQL skills. I found 56 positions that match your criteria. You can review them now.

Sure. Here's the analysis:

Job Analysis:

The Senior Data Engineer role at Galent is focused on developing and optimizing data transformation pipelines utilizing Snowflake and DBT technologies. The core purpose of this position is to enable efficient and scalable data processing that drives actionable insights within the company's analytics ecosystem. The ideal candidate will not only design and implement reusable DBT models but also enhance the overall performance and reliability of Snowflake by leveraging its advanced features. Individuals in this role will encounter challenges such as balancing performance optimization with cost management, troubleshooting data quality issues, and ensuring compliance through role-based access control. Success will be measured by the engineer's ability to deliver high-quality, timely data transformations and maintain robust documentation and metadata management, complete with adherence to organizational standards. Strong technical acumen in SQL and data modeling, paired with effective communication skills for cross-functional collaboration, will be essential for thriving in this position. Furthermore, familiarity with SAP data sources indicates that understanding complex data environments will be crucial for successful execution of responsibilities.

Company Analysis:

Galent is positioned as an innovative yet established player in the AI-native digital engineering space, focused on transforming enterprises through advanced AI solutions. This environment suggests a dynamic atmosphere where adaptability and forward-thinking are essential. The company's emphasis on cutting-edge AI technologies empowers its clients, which aligns with the role of a Senior Data Engineer whose work directly influences the performance of these transformative solutions. Culture appears to value collaboration, technical excellence, and continuous improvement, reflecting Galent's mission to enable organizations to thrive. Given that the data engineering team will work closely with other functional units to integrate complex data from various sources, including SAP, this role is likely to provide substantial visibility, offering opportunities for contributions at both a tactical and strategic level. The overall strategic alignment of this role points toward reinforcing Galent’s goal of delivering scalable AI-enabled solutions, suggesting that the candidate will play a pivotal part in the company’s growth and innovation trajectory.

Absolutely. Here are some mock interview questions that could come up:

• Tell me about a time when you had to optimize a data pipeline for better performance. What steps did you take and what was the outcome?
• Describe a challenging data quality issue you encountered. How did you identify it and what did you do to resolve it?
• Can you share an experience where you had to work with a cross-functional team to deliver a data engineering solution? What was your role?
• How would you approach designing and implementing a new DBT project from scratch? What key elements would you prioritize?
• What strategies have you used to monitor and optimize warehouse performance in Snowflake?
• Explain how you would implement role-based access control in a Snowflake environment. What are the best practices to follow?
• Imagine you have a scenario where data from multiple SAP systems needs to be integrated into Snowflake for analytics. How would you handle this task?
• Suppose you notice irregularities in data processed through your pipelines. What steps would you take to troubleshoot and fix the issues?
• Why are you interested in working at Galent specifically? How do you think your experience aligns with our mission?
• How do you stay updated with advancements in data engineering technologies, particularly concerning AI and data platforms?
• What do you view as the biggest challenge facing companies today as they integrate AI solutions into their data architectures?
• Can you elaborate on how you incorporate CI/CD processes in your data engineering workflows? What tools do you find most effective?
• What questions do you have for us about our approach to data engineering and the technologies we utilize?
• How would you ensure that your documentation practices align with best practices in data lineage and metadata management?