As a Senior Data Warehouse Developer/Analyst at MathWorks, your primary responsibilities will include designing and developing sophisticated data warehouse software and data mart solutions. You'll also conduct thorough testing to ensure quality, troubleshoot issues, and provide 24/7 on-call support every two months. Collaborating closely with stakeholders, you'll analyze structured and semi-structured data and address integration needs.

To succeed as a Senior Data Warehouse Developer/Analyst at MathWorks, it’s essential to have experience in data modeling and ETL processes, along with proficiency in BusinessObjects Data Services. Moreover, a background in software development and familiarity with data profiling will be advantageous for this role.

The hybrid work model at MathWorks allows Senior Data Warehouse Developer/Analysts to enjoy the flexibility of working both in-office and remotely. This balance enables better collaboration with colleagues while providing the convenience to optimize your work-life experience at home.

The compensation range for the Senior Data Warehouse Developer/Analyst position at MathWorks typically falls between $100,000 and $140,000, reflecting the expertise and valuable contributions expected from candidates in this role.

Essential skills for a Senior Data Warehouse Developer/Analyst at MathWorks include strong analytical capabilities, proficiency in data modeling and ETL processes, and experience with data integration tools like BusinessObjects Data Services. Additionally, familiarity with both structured and semi-structured data will be incredibly beneficial.

When discussing your experience with data modeling, focus on specific projects where you successfully implemented models that met user requirements. Highlight the tools you used, the types of data you worked with, and how your modeling improved data access and insights.

Be ready to discuss the ETL tools you’ve worked with, particularly BusinessObjects Data Services if applicable. Explain how you used these tools to extract, transform, and load data, focusing on specific achievements and the impact of your work on data quality and accessibility.

Discuss your approach to data quality assurance, including the testing processes you implement. Talk about how you validate and clean data, and reference any specific methodologies or tools that help maintain high standards in your projects.

Provide a thorough account of a challenging data integration project, explaining the specific challenges you faced, the steps you took to overcome them, and the results of your efforts. Demonstrating problem-solving skills and successful outcomes is key to showcasing your capabilities.

Discuss your experience with performance tuning by providing examples of techniques or strategies you’ve employed to enhance the performance of data warehouse solutions. Focus on specific metrics or improvements achieved through your efforts.

Outline your troubleshooting process in handling data-related issues. Emphasize your analytical skills, detail how you diagnose problems, and discuss your approach to communicating with stakeholders to resolve issues efficiently.

Share experiences where you provided mentorship, emphasizing techniques you use to support juniors in their learning. Discuss specific mentoring moments that highlight your leadership and effort to build a collaborative environment.

Discuss the importance of communication and active listening when collaborating with stakeholders. Highlight strategies you use to ensure that stakeholders’ requirements are clearly understood and that you stay aligned with their goals throughout development.

Narrate an example showcasing a successful data mart project you led, touching on the requirements, your approach to design and implementation, as well as the value it provided to your organization in terms of insights and data accessibility.

Finish with discussing the resources you use to stay informed on data warehousing trends, like industry conferences, online courses, or relevant publications. Clarifying your commitment to continuous learning signals your dedication to the field.