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Updated weekly · Last refresh Nov 11

Torq Data Engineer Interview Questions

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

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1
PipelinesStart here. 8 questions · ~65 min
Design an ETL Pipeline for Large DatasetsHard

Design an ETL pipeline to process 10TB of data daily from multiple sources into a data warehouse with strict data quality checks.

InfrastructureETLData ModelingTorq
Data Modeling Tools and ArchitectureMedium

Preferred tools and patterns for data modeling and pipeline architecture in a modern data platform.

InfrastructureToolsData ModelingTorq
ETL vs ELT Trade-offsEasy

Compare ETL and ELT, and explain when ELT is the better pipeline pattern.

ETLELTData ModelingTorq
Batch vs Stream Processing Trade-offsMedium

Compare batch and stream processing across latency, complexity, cost, and data quality in a modern analytics pipeline.

InfrastructureStream ProcessingETLTorq
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2
Behavioral & Leadership3 questions · ~24 min
Leading a High-Stakes Cross-Functional ProjectMedium

Tests leadership through execution: ownership, prioritization, and stakeholder alignment on a meaningful project with measurable outcomes.

Stakeholder ManagementLeadershipOwnershipTorq
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3
More topics3 questions · ~24 min
SQL Metrics ExtractionMedium

Tests practical SQL skills for aggregations, filtering, and metric extraction.

Data WranglingGroup ByAggregationsTorq
Python Data Cleaning ScriptMedium

Tests ability to build reliable data processing code with cleaning and validation steps.

Hash TablesStringsSortingTorq
Optimizing a Slow SQL QueryHard

Tests performance troubleshooting using query plans, indexing, and query rewrites.

SubqueriesJoinsData WranglingTorq

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
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
Python or R Analysis LibrariesMedium
Practice
Practice drill

Count unique respondents by Python or R library and return the most commonly used libraries first.

analytics toolsData ManipulationData WranglingOklahoma City Thunder
The finish line: interview-readyComplete all 14 questions plus 3 hands-on drills to finish this plan.