Senior Data Analyst

The Senior Data Analyst at cBEYONData is responsible for managing and mentoring junior analysts, leading functional data expertise, managing documentation of functional specifications, and coordinating end-user data validation activities. The role requires collaboration with Master Data Governance Architects to implement data governance policies, ensuring high data quality and compliance with federal standards.

To apply for the Senior Data Analyst role at cBEYONData, candidates must have an active DoD Secret security clearance, a bachelor's degree in a related field, and at least 6 years of relevant work experience. Strong analytical and communication skills and expertise in data management tools like Collibra are also critical for success in this position.

cBEYONData offers a remote work environment for the Senior Data Analyst role, promoting flexibility and a dynamic work culture. You'll collaborate with various stakeholders and cross-functional teams, all while driving substantial contributions to IT transformation initiatives, making it a stimulating and supportive workplace.

As a Senior Data Analyst at cBEYONData, you will work on a multi-year SAP S/4 HANA modernization effort for a federal client, focusing on IT transformation projects, data governance practices, and providing support for large-scale government ERP implementations that ensure data consistency and accuracy.

While not mandatory, previous experience supporting Department of Defense clients, especially the Army, is preferred for the Senior Data Analyst position at cBEYONData. This experience can provide valuable insights and enhance your ability to navigate the specific challenges encountered in this sector.

When responding to questions about data governance, share specific examples of your past roles where you established or maintained data governance frameworks. Highlight how you ensured compliance with regulatory standards and how your efforts improved data quality across the organization.

To answer this question effectively, discuss your organizational strategies, such as creating a priority matrix or using project management tools. Give examples of how you’ve successfully managed competing deadlines while maintaining high-quality outputs.

Discuss your direct experience with SAP S/4 HANA or comparable ERP systems. Focus on your understanding of data integration, the processes you participated in, and the specific contributions you made to enhance data management during ERP implementations.

When discussing mentoring, share your philosophy on leadership and specific strategies you’ve employed to foster a collaborative environment. Provide examples of how you’ve helped junior analysts grow their skills and contribute effectively to team goals.

Talk about the data management tools you're familiar with, including Collibra or any others you've used extensively. Outline what you find beneficial about these tools and how they enable efficient data governance and management practices.

Explain your approach to data validation, emphasizing collaboration with stakeholders and integrating business rules. Share examples of how you have managed or improved validation activities and ensured data accuracy in your prior roles.

Detail a specific instance where you faced a challenging data-related issue. Walk through how you analyzed the problem, the methodology you employed to resolve it, and the positive impact your solution had on the project or organization.

Highlight your ability to simplify data concepts using clear, relatable terms. Discuss any methods, such as visuals or storytelling, to make complex data more accessible to non-technical audiences, ensuring their understanding and engagement.

Discuss potential challenges such as compliance issues, data security requirements, and the complexity of integrating various data sources. Share your thoughts on strategies to mitigate these challenges and maintain data integrity in federal projects.

Touch on your commitment to continuous improvement by discussing methodologies like Agile or Lean approaches. Provide examples of how you’ve implemented changes in data management processes to enhance quality and efficiency over time.