SSAS Developer

As an SSAS Developer at Master-Works, your main responsibilities include designing and implementing analytical solutions, enhancing SSAS OLAP cubes, and developing multidimensional and tabular models. You will also be tasked with creating and maintaining MDX and DAX queries, ensuring data quality in collaboration with data warehousing teams, and optimizing existing SSAS solutions for better performance.

To apply for the SSAS Developer position at Master-Works, candidates should possess a Bachelor's degree in Computer Science, Information Technology, or a related field. Additionally, candidates should have a minimum of 2 years of hands-on experience in SSAS development, including proficiency in writing and optimizing MDX and DAX queries.

Preferred skills for the SSAS Developer role at Master-Works include familiarity with Power BI or other data visualization tools, experience with ETL processes and tools, and relevant Microsoft Certifications in Data and Analytics. These skills can significantly enhance your contribution to our analytics team.

At Master-Works, the work environment for an SSAS Developer is fast-paced and collaborative, emphasizing teamwork to drive results. You will work closely with various stakeholders, which allows for a dynamic exchange of ideas and an opportunity to develop innovative analytical solutions.

The onboarding process for new SSAS Developers at Master-Works includes a comprehensive introduction to our analytics team and processes. You’ll receive mentorship and guidance from experienced team members, ensuring you are well-equipped to handle your responsibilities effectively and integrate smoothly into our company culture.

When answering this question, provide specific examples of your work with SSAS, detailing your responsibilities, the models you developed, and the technologies used. Highlight your success in optimizing performance and how your contributions impacted business insights.

To effectively answer this question, describe OLAP cubes as multidimensional data structures that allow data to be viewed from multiple perspectives and tabular models as more straightforward, simpler structures based on relational data. Discuss scenarios where you might choose one over the other.

Discuss various strategies for optimizing MDX or DAX queries, such as reducing the complexity of calculations, ensuring data is pre-aggregated where possible, and using efficient data retrieval techniques. Providing a specific example from your experience could strengthen your response.

Share your approach to ensuring data quality during integration, such as validating data comprehensively, setting up ETL processes, and collaborating closely with data warehousing teams. Emphasize the importance of accuracy and efficiency in these steps.

When addressing this question, detail a specific project, the challenges you faced, the actions you took to resolve these issues, and the positive outcomes that resulted, illustrating your problem-solving skills and team collaboration.

Talk about your standardized processes for documenting technical specifications, whether through formal documentation tools, contributing to internal wikis, or maintaining a central repository of resources. Stress the importance of clear and accessible documentation for team collaboration.

In your response, highlight the critical role of SSAS in enabling businesses to perform complex data analysis and derive meaningful insights, explaining how it supports business intelligence initiatives and data-driven decision-making.

Indicate your commitment to continuous learning by mentioning any relevant certifications, online courses, forums, or communities you are a part of. This shows your dedication to staying current with industry trends and technological advancements.

Provide an example where you effectively communicated technical information to non-technical stakeholders, focusing on how you bridged any gaps and ensured mutual understanding to align project goals and expectations.

Convey that essential qualities include strong analytical skills, attention to detail, collaborative spirit, effective communication, and a problem-solving mindset. You may also mention a passion for data analytics as a key motivating factor.