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Updated weekly · Last refresh Aug 30

Rystad Energy Data Engineer Interview Questions

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

19questions
~3htotal time
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1
CodingStart here. 4 questions · ~36 min
Longest Substring Without RepeatsMedium
Practice

Find the longest substring with all unique characters using a sliding window.

Hash TablesStringsTwo PointersRystad Energy
Explain Time ComplexityEasy

Tests ability to analyze algorithm efficiency and communicate tradeoffs.

MathArraysRystad Energy
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2
Pipelines9 questions · ~81 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityRystad Energy
Design Real-Time Feedback Ingestion PipelineMedium

Design a real-time pipeline for ingesting human feedback events with validation, replay, and support for evolving schemas.

data pipelinereal-time ingestiondata architectureRystad Energy
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 ModelingQualityRystad Energy
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3
SQL & Data Manipulation5 questions + 3 drills · ~75 min
Data Normalization ImportanceEasy

Tests knowledge of normalization principles and their impact on data integrity and redundancy.

JoinsData WranglingRystad Energy
Design a New Application SchemaMedium

Tests database design fundamentals including entities, relationships, and constraints.

JoinsData WranglingData ModelingRystad Energy
SQL vs NoSQL DifferencesMedium

Tests understanding of data storage trade-offs and when to choose different database paradigms.

nosqlsqlRystad Energy
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
Resolve Conflicting Customer AttributesMedium
Practice
Practice drill

Use joins, CTEs, and row ranking to resolve conflicting customer profile values across ACME House systems.

JoinsData WranglingQualityRamsey SolutionsACME House
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4
More topics1 question · ~9 min
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The finish line: interview-readyComplete all 19 questions plus 3 hands-on drills to finish this plan.