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Updated weekly · Last refresh Sep 25

Torq Data Engineer Interview Questions

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

15questions
~2htotal time
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1
PipelinesStart here. 9 questions · ~73 min
Design an ETL Pipeline for Large DatasetsMedium

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 ArchitectureEasy

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

InfrastructureToolsData ModelingTorq
Cloud AI Deployment Pipeline ExperienceMedium

Discuss experience building cloud-based AI pipelines, including orchestration, processing patterns, infrastructure choices, and data quality controls.

InfrastructureToolsQualityTorq
ETL vs ELT Trade-offsEasy

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

ETLELTData ModelingTorq
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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
Cleaning a Messy DatasetMedium
Practice
Practice drill

Filter invalid game events and normalize event names using PostgreSQL string and null handling.

missing valuesdata cleaningnull handlingQuantiumScopelyCarvana
The finish line: interview-readyComplete all 15 questions plus 3 hands-on drills to finish this plan.