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Updated weekly · Last refresh Oct 17

Circana Data Engineer Interview Questions

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

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1
SQL & Data ManipulationStart here. 7 questions + 2 drills · ~78 min
Top 3 Products by CategoryMedium
Practice

Use a CTE and ROW_NUMBER to return the top three Rakuten products by completed-order revenue in each category.

Window FunctionsRankingCTEsCircana
Window Ranking in Ticket QueuesMedium

Explain SQL window functions and when to use ROW_NUMBER() versus DENSE_RANK() for ranked ticket analysis.

Window FunctionsRankingrow_numberCircana
Broadcast vs Sort-Merge JoinsMedium

Assesses your knowledge of join strategies and when to apply them for performance.

JoinsRankingData WranglingCircana
Optimize SQL and Explain PlansHard

Tests your SQL tuning skills using execution plans to improve performance.

JoinsCTEsAggregationsCircana
Handling Missing DataEasy
Practice
Practice drill

Replace missing sales fields with defined defaults and return a consistently ordered dataset.

missing valuesdata cleaningnull handlingUniversity of MinnesotaThe Home DepotNationwide
Analyze Customer Purchase Trends with Window FunctionsEasy
Practice
Practice drill

Calculate the monthly spending trends for customers using window functions and joins.

The Home DepotAvisoHewlett Packard Enterprise
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2
Pipelines43 questions · ~354 min
Design ETL Pipeline for Retail Sales DataMedium

Build an ETL pipeline to process 10M daily retail transactions into a data warehouse with strict data quality and latency requirements.

Circana
Choosing Row vs Column FormatsMedium

How to choose between row-oriented and column-oriented formats across different stages of a data pipeline.

performanceCloudData ModelingCircana
Model Analytics Warehouse for RetailEasy

Design an ELT pipeline and warehouse data model in Snowflake for retail analytics, including dimensional modeling, orchestration, and data quality.

InfrastructureData ModelingQualityCircana
Handle PySpark Data SkewMedium

Approach for detecting and mitigating skew in PySpark pipelines using partitioning, join strategies, and runtime monitoring.

Data Qualitypysparkdata skewnessCircana
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