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Updated weekly · Last refresh Aug 30

ZEISS Group Data Engineer Interview Questions

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

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1
CodingStart here. 4 questions · ~35 min
First Non-Repeating CharacterEasy
Practice

Find the first non-repeating character in a string using frequency counting and a second pass in O(n) time.

Hash TablesStringsSearchingZEISS Group
Parse Logs Efficiently in PythonMedium

Tests efficient file parsing, pattern extraction, and memory-aware Python implementation.

StringsfunctionsZEISS Group
Data Engineering Problem SolvingMedium

Tests end-to-end problem solving and practical data engineering troubleshooting.

data engineeringProblem SolvingZEISS Group
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2
SQL & Data Manipulation5 questions + 3 drills · ~74 min
Ranking Trial Results with Window FunctionsEasy

Explain how RANK(), DENSE_RANK(), and ROW_NUMBER() differ when ordering tied clinical trial results.

Window FunctionsRankingData WranglingZEISS Group
Optimize Slow Join and AggregationsHard

Tests query plan reasoning, indexing strategy, and performance optimization for complex SQL.

JoinsperformanceAggregationsZEISS Group
Schema for Irregular Sensor DataHard

Tests data modeling for time-series events and normalization for irregular measurements.

financial datadata integrityData ModelingZEISS Group
Rank Top Service CategoriesEasy
Practice
Practice drill

Use RANK() and grouped revenue to find ServiceTitan's top three service categories in each region.

RankingperformancesqlServiceTitan
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
Weekly Service KPI by TeamMedium
Practice
Practice drill

Analyze weekly service delivery performance by team using joins, a CTE, date aggregation, and KPI calculations.

KPIJoinssqlDeloitteReputation
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3
Behavioral & Leadership16 questions · ~139 min
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4
More topics2 questions · ~17 min
Data Quality and Schema EvolutionMedium

Approach for handling schema changes and data quality checks in a high-volume data lake pipeline.

schema evolutionData ModelingQualityZEISS Group
Ingest High-Throughput IoT DataHard

Tests end-to-end ingestion design for scale, throughput, and reliability in ZEISS manufacturing data.

InfrastructureStream ProcessingETLZEISS Group

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