Top 35
Prep plan
Updated weekly · Last refresh Aug 30

lululemon Data Engineer Interview Questions

The questions to prepare for a lululemon Data Engineer interview. Questions from real interview reports rank first. Updated weekly.

35questions
~5htotal time
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1
PipelinesStart here. 25 questions · ~201 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualitylululemon
Choosing Batch vs Real TimeHard

Evaluate when a pipeline should use stream processing versus scheduled batch based on latency, cost, complexity, and data quality needs.

Stream ProcessingBatch ProcessingDependencieslululemon
Handle Missing Values in ETLEasy

Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.

Data WranglingETLQualitylululemon
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2
Coding3 questions · ~24 min
Optimizing Out-of-Memory PythonHard

Tests ability to optimize Python memory usage and performance for large-scale data processing.

Hash TablesArraysGreedylululemon
Pandas or PySpark Data Cleaning and TransformsMedium

Tests practical data processing skills and performance-aware use of Python data tools.

Hash TablesArraysSortinglululemon
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3
SQL & Data Manipulation7 questions + 3 drills · ~86 min
Production-Grade SQL for Large DatasetsMedium

Tests SQL performance, correctness, and practical techniques for large data transformations.

Window FunctionsJoinsAggregationslululemon
Technical Round ExpectationsEasy

Tests familiarity with the interview's technical scope across SQL, Python, modeling, and warehousing.

SQL & Data ManipulationJoinsAggregationslululemon
Complex Joins, Windows, and AggregationsMedium

Tests advanced SQL skills for analytical queries and correct handling of complex transformations.

Window FunctionsJoinsAggregationslululemon
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
Handle Missing Financial Input ValuesMedium
Practice
Practice drill

Use joins, CTEs, and CASE logic to flag and fill missing financial fields for Qlik planning records.

Data WranglingETLQualityQlik
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The finish line: interview-readyComplete all 35 questions plus 3 hands-on drills to finish this plan.