Senior Data Analyst

As a Senior Data Analyst at Sheetz, your key responsibilities include leading data-driven insights that support strategic business opportunities, designing and implementing effective data strategies, and mentoring other data professionals. You will oversee advanced analytical projects and present your findings to senior management, making it essential to communicate your insights clearly and effectively.

To qualify for the Senior Data Analyst role at Sheetz, you should have a Bachelor’s degree in fields like Economics, Statistics, or Computer Science. An MBA or advanced degree is preferred. Additionally, a minimum of five years of experience in data modeling, optimization, and application to business strategies is required, along with an understanding of advanced analytics techniques.

At Sheetz, we value growth! As a Senior Data Analyst, you'll have the opportunity to develop and mentor future data-driven leaders within the organization. Additionally, you'll be responsible for creating training curriculums for Data Analysts across departments, fostering a culture of continuous learning and advancement.

In the Senior Data Analyst role at Sheetz, you'll utilize a variety of tools and technologies for data mining, machine learning, and business intelligence. Your expertise will enable you to design information solutions and optimize data access and visualization processes efficiently.

The work environment for a Senior Data Analyst at Sheetz is collaborative and innovative. You'll work closely with various departments and senior management, fostering a data-driven decision-making culture. We're committed to inclusivity, ensuring that every team member has the resources and support needed to excel.

When discussing your experience with data modeling, focus on specific projects where you utilized models to drive decision-making. Highlight the processes you used, the outcomes, and how your insights influenced strategic business moves.

Describe your favorite tools and techniques, such as SQL for querying, Python for data manipulation, or Tableau for visualization. Articulate why these tools are effective for you and how they contribute to your analytical methods.

Share an example where you faced conflicting data interpretations. Discuss the steps you took to gather further insights, communicate with stakeholders, and ultimately align all parties on a common understanding to enhance decision-making.

Highlight your mentoring style, emphasizing key areas such as providing actionable feedback, encouraging questions, and offering hands-on guidance with analytics tools. Provide examples of how mentoring has enriched your team’s performance.

Prepare a concise story focusing on the background, your analytical methods, and the resultant change. Emphasize how your insights were received and any measurable impact they had on the business.

Discuss your organizational skills and the methods you employ to prioritize tasks—such as urgency, impact, and stakeholder needs. Mention any tools or systems you use to keep track of deadlines and project statuses.

Be ready to detail a specific analytical problem, your thought process, and the techniques you employed to arrive at a solution. Focus on the skills you leveraged and the outcome of your efforts.

Share your methods for staying current, such as attending industry conferences, participating in online courses, following key thought leaders on social media, or engaging in professional data science communities.

Discuss KPIs relevant to the business context, such as customer satisfaction metrics, sales growth, or operational efficiency indicators. Justify why these KPIs are essential for effective business assessment.

Talk about your approach to data validation, cleansing, and verification processes. Share examples of checks you perform and the importance of data integrity in developing reliable analyses.