To succeed in this rigorous 6-to-7 round process, you must be deeply prepared across several core competencies. Interviewers will probe your technical depth, your statistical intuition, and your ability to drive marketing strategy.
SQL and Data Manipulation
SQL is the foundational language for any data role at lululemon. You will be evaluated on your ability to extract, clean, and transform complex datasets efficiently. Strong performance means writing optimized queries that handle edge cases, utilizing advanced window functions, and demonstrating a clear understanding of relational database structures.
Be ready to go over:
- Complex Joins and Aggregations – Understanding how to merge multiple marketing data sources (e.g., CRM data with web analytics) without duplicating records.
- Window Functions – Using functions like
ROW_NUMBER(), RANK(), and LEAD()/LAG() to analyze sequential guest behavior over time.
- Data Cleaning – Handling nulls, casting data types, and standardizing inconsistent marketing campaign tags.
- Advanced concepts (less common) – Query optimization, indexing strategies, and writing CTEs for highly complex, multi-step data transformations.
Example questions or scenarios:
- "Write a query to find the top 10% of guests by lifetime value, and calculate their average order frequency over the last 12 months."
- "How would you identify guests who interacted with an email campaign but did not make a purchase until they visited a physical store?"
- "Given a table of daily ad spend and a table of daily conversions, write a query to calculate the rolling 7-day Return on Ad Spend (ROAS)."
Statistical Analysis and Experimentation
Marketing analytics relies heavily on understanding causality and variance. Interviewers will test your grasp of statistics to ensure you can accurately measure campaign performance and design robust A/B tests. A strong candidate will know not just how to run a test, but the underlying assumptions and potential pitfalls of the methodology.
Be ready to go over:
- A/B Testing Frameworks – Designing experiments, selecting appropriate sample sizes, and defining minimum detectable effects.
- Statistical Significance – Explaining p-values, confidence intervals, and Type I vs. Type II errors in a business context.
- Hypothesis Testing – Formulating null and alternative hypotheses for marketing interventions.
- Advanced concepts (less common) – Multi-armed bandit testing, causal inference models, and propensity score matching for observational data.
Example questions or scenarios:
- "Walk me through how you would design an A/B test for a new promotional banner on the lululemon homepage."
- "If an A/B test shows a significant increase in click-through rate but no change in conversion rate, how do you interpret this, and what do you recommend?"
- "Explain p-value to a marketing manager who has no background in statistics."
Data Visualization and Storytelling
Having the right data is only half the battle; you must be able to communicate it effectively. You will be evaluated on your proficiency with BI tools (like Tableau or PowerBI) and your ability to design intuitive, actionable dashboards. Strong performance involves choosing the right chart types, minimizing cognitive load, and highlighting the "so what" for stakeholders.
Be ready to go over:
- Dashboard Design Principles – Structuring information hierarchically and designing for the end-user's specific needs.
- Metric Selection – Choosing the right KPIs to display for different levels of leadership (e.g., tactical vs. strategic).
- Visualizing Trends – Effectively showing performance against targets, year-over-year comparisons, and cohort analyses.
- Advanced concepts (less common) – Custom calculated fields in Tableau, LOD expressions, and dashboard performance optimization.
Example questions or scenarios:
- "Describe a time you built a dashboard that changed a stakeholder's mind or drove a specific business action."
- "If you were tasked with building a weekly marketing performance dashboard for the executive team, what 5 metrics would you include and how would you visualize them?"
- "How do you handle a situation where a stakeholder asks for 20 different metrics on a single dashboard?"
Past Project Walkthrough
Because this role requires high autonomy, interviewers will dedicate significant time to dissecting a project you have completed. They are evaluating your end-to-end ownership, your problem-solving framework, and your ability to navigate roadblocks. A strong performance requires a structured narrative (like the STAR method) that clearly delineates your specific contributions.
Be ready to go over:
- Problem Definition – How you identified the business problem and aligned with stakeholders on the objective.
- Methodology – The technical steps you took, the tools you used, and why you chose that specific approach.
- Impact and Reflection – The quantifiable business outcome of your work and what you would do differently next time.
Example questions or scenarios:
- "Walk me through a complex analytics project you led from inception to delivery. What was the hardest technical challenge you faced?"
- "Tell me about a time your data contradicted the marketing team's intuition. How did you handle the conversation?"
- "Explain a project where you had to piece together fragmented data to tell a cohesive story."