Data Analyst, Sales & Brand Partnerships

As a Data Analyst for Sales & Brand Partnerships at LTK, you'll be tasked with providing actionable insights based on data analysis, collaborating with marketing and sales teams, and developing insightful dashboards. Your role will entail forecasting business growth opportunities and presenting your findings clearly to various stakeholders, ensuring that strategic decision-making is data-driven.

To be considered for the Data Analyst role at LTK, candidates should possess a Bachelor’s degree and a minimum of 4 years of analytical experience. Strong analytical skills, proficiency in SQL, and the ability to work with visualization tools like Looker or Tableau are crucial. Effective communication skills to present data insights clearly are essential for success in this role.

The Data Analyst at LTK plays a pivotal role in driving growth by identifying trends through data analysis and delivering forecasts that inform business strategies. By collaborating with various teams to develop metrics and insightful dashboards, the analyst ensures the business capitalizes on revenue opportunities and measures its performance effectively.

In the Data Analyst position at LTK, you will utilize enterprise BI tools to create insightful dashboards. Familiarity with SQL is required for querying databases, and working knowledge of visualization tools such as Looker and Tableau will help in presenting complex data in an understandable format for stakeholders.

LTK fosters a remote-first, productivity-driven culture, which is an attractive feature for Data Analysts looking to balance work and life. Additionally, LTK offers competitive compensation, a diverse work environment, and opportunities to drive impactful decisions, making it an exciting place to grow your analytical career in the creator commerce industry.

When answering this question, focus on specific experiences where you utilized data analysis to drive business results. Highlight any particular tools or methodologies you used and discuss the impact of your analysis on key business decisions.

Explain your strategy for tailoring your insights based on your audience's needs. Discuss the importance of clear visuals and concise language to communicate findings effectively, and share any previous experiences where your presentations led to actionable decisions.

Provide a list of BI tools you've worked with, such as Tableau or Looker, and describe how you used these tools to create dashboards or reports that aided in data-driven decision-making. Emphasize your comfort level with these technologies and any specific projects where they made a significant impact.

Discuss your methodology for working with large datasets, including any tools or techniques you employ to clean, analyze, and visualize the data. Highlight past experiences where you successfully managed complex data and what outcomes were achieved.

Share a specific instance where your data analysis revealed a trend that led to actionable insights. Describe the process you followed, the tools you used, and the resulting business decisions that arose from your findings.

Explain how you build relationships with teams across the organization to share insights and gather information. Provide examples of fruitful collaborations that enabled data-driven strategies that aligned with business goals.

Detail a project where you utilized SQL to extract and analyze data. Focus on the data sources you worked with, the queries you designed, and how your analysis contributed to business outcomes.

Discuss your methods for staying updated on industry trends, whether it's through attending webinars, participating in online courses, or reading relevant publications. This demonstrates your commitment to professional development and adapting to new tools and techniques.

Share a few key metrics that you've found useful in previous roles. Discuss how you determine which metrics to focus on for different teams and how those measurements inform successful business strategies.

Emphasize the importance of data validation in your analysis process. Describe your routine for checking data quality and any methodologies you use to identify errors or inconsistencies in the data.