Senior Data Analyst

As a Senior Data Analyst at Engine, you will lead key analytics projects while partnering with various stakeholders to define metrics and reporting structures. Your role will include deriving actionable insights to enhance product and business performance, designing statistical analyses, participating in growth experiments, and managing business intelligence reporting dashboards.

To be a successful Senior Data Analyst at Engine, candidates should hold a Bachelor’s degree in a quantitative field such as Data, Economics, or Statistics, and possess at least 4 years of relevant experience. Proficiency in SQL, a strong foundation in statistics, and excellent communication skills are also essential.

Senior Data Analysts at Engine commonly work with SQL to manipulate data, and tools like Looker, Snowflake, and Amplitude for data visualization and analysis. Familiarity with these tools will help you excel in delivering impactful insights and analyses.

Engine believes in supporting its employees' work-life balance through a hybrid-hub model. This allows Senior Data Analysts to work fully remotely or in one of our vibrant offices, providing flexibility and the right environment for success.

Engine offers ample opportunities for growth and development for Senior Data Analysts. You will have the chance to lead valuable projects and collaborate with leadership, directly influencing key business decisions and shaping the future of the company.

When answering this question, consider discussing a specific project where you faced data quality issues or tight deadlines. Explain how you approached the analysis, the methodology you used, and the impact your insights had on business decisions.

Discuss your approach to data validation and cleaning, including the tools you use to check for errors and inconsistencies. Highlight the importance of accuracy and how maintaining data integrity impacts the effectiveness of your analyses.

Explain which statistical techniques you are most comfortable with, such as regression analysis or hypothesis testing. Discuss examples of how you applied these methods in past projects and the outcomes they produced.

Share your strategies for managing time and prioritizing tasks, possibly mentioning the use of project management tools or techniques like Eisenhower Matrix. Emphasize your ability to communicate with stakeholders to align on expectations.

Detail your hands-on experience with visualization tools like Looker or Tableau. Discuss how you used these tools to create dashboards or reports that helped stakeholders understand data insights clearly.

Highlight your communication and collaboration skills. Share examples of working with product managers, engineers, and marketing teams to ensure data-driven decision-making while adapting insights to meet their needs.

Provide a specific instance where your analysis led to a key business decision. Describe the data you presented, the insights derived, and how they drove actionable changes for the organization.

Talk about your commitment to continual learning through resources such as online courses, webinars, or attending industry conferences. Mention any relevant communities you engage with to exchange knowledge.

Identify key performance indicators (KPIs) that are crucial in your current or past roles. Discuss why these metrics matter and how they align with the strategic goals of the business.

Discuss your openness to feedback and view it as a collaborative process that enhances your analyses. Share an example of a time you incorporated feedback into your work to produce a better outcome.