Master Data Analyst

As a Master Data Analyst at CUBE, your key responsibilities include ensuring the integrity of master data, implementing Master Data Management capabilities, and maintaining data quality. You will work collaboratively with different teams, especially the Data Quality team, to identify and resolve data issues. Additionally, you'll define and apply guidelines on data ownership and coding structures to enhance overall data usability across the organization.

CUBE is looking for candidates with proficiency in SQL (2+ years), experience in data modeling, cleansing, and enrichment, as well as advanced skills in MS Excel. Ideal candidates should also possess high-level communication skills, strong problem-solving abilities, and a trustworthy judgment. Familiarity with Master Data Management, ETL tools like SSIS, and databases such as MongoDB or Azure Cosmos DB is also desirable.

At CUBE, we foster a 'make it happen' culture that promotes personal and professional development from day one. As a Master Data Analyst, you'll collaborate with some of the brightest minds in the industry and have access to continuous learning opportunities through workshops and training programs, helping you grow your skills and take ownership of your career.

CUBE boasts a diverse and collaborative culture that values teamwork and purpose. As a Master Data Analyst, you’ll be part of a fast-paced, high-performing team where innovation is encouraged. Our commitment to inclusivity and diversity ensures that every employee feels valued and inspired to contribute to our shared mission of transforming regulatory compliance.

As a Master Data Analyst at CUBE, you will work with advanced technologies including SQL databases, ETL tools like SSIS, and potentially cloud environments such as Microsoft Azure. You'll also leverage Microsoft SQL Server and collaborate with teams using the Atlassian suite to enhance project management and teamwork.

When answering this question, focus on specific examples of how you've used SQL to manage and manipulate data in previous roles. Share details about the types of queries you wrote, the databases you worked with, and any insights you gained from your analyses that helped your team or organization achieve its goals.

Discuss the steps you take to maintain data quality, including setting standards for data entry, conducting regular audits, and collaborating with teams to address any discrepancies. Highlight any tools or practices you’ve implemented to enhance data governance and quality assurance.

Share a specific example of a data issue you encountered, detailing the context and the complexity involved. Explain your approach to troubleshooting, the collaboration with colleagues or teams, and the final outcome, emphasizing what you learned from the experience.

In your answer, outline your familiar methods and tools for data cleansing, such as validation checks and deduplication processes, as well as how you enrich data through external sources or improved data collection methods. Provide examples to illustrate your approach.

Discuss the resources you utilize to stay informed, such as industry blogs, webinars, or networking events. Mention any specific communities you engage with or conferences you attend to enhance your knowledge and skills related to data management technology.

Provide a comprehensive overview of your experience with MDM, including specific tools or software you’ve used, the scope of MDM projects you’ve managed, and how these experiences have informed your understanding of data integrity and governance.

Explain your communication and teamwork approach when collaborating with the Data Quality team. Discuss how you would ensure alignment on data standards and quality objectives, and how you would handle any conflicts or discrepancies in data findings.

Share a specific instance where you identified a process improvement opportunity related to data management. Detail what steps you took to implement changes and the impact it had on the team's efficiency or data quality, supported by metrics if possible.

In answering this question, provide details on the types of data models you've created, such as logical, physical, or conceptual models. Highlight how these models contributed to the organization’s understanding of data relationships and facilitated better data decisions.

Discuss your time management strategies, such as prioritization and breaking tasks into manageable steps. Provide examples of past situations where you successfully met tight deadlines while ensuring data quality and stakeholder satisfaction.