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BSRI Solutions Data Engineer Interview Questions

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

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1
PipelinesStart here. 5 questions · ~40 min
Schema Evolution in Data LakesHard

Design a safe schema evolution strategy for distributed data lakes and warehouses without breaking downstream ETL or analytics.

Data Qualityschema evolutiondata warehouseBBSRI Solutions
Error Handling and Logging in PipelinesHard

Explain how to design reliable error handling, structured logging, retries, alerts, and auditability for data pipelines.

Data Qualityerror handlingdata pipelinesBBSRI Solutions
Design Petabyte-Scale Log Streaming PipelineHard

Design a Databricks-native real-time log pipeline processing 1.5-3 PB/day with sub-90-second latency, replayability, and strong data quality controls.

InfrastructureStream ProcessingQualityBBSRI Solutions
Batch vs Stream Processing Trade-offsMedium

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

InfrastructureStream ProcessingETLBBSRI Solutions
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityBBSRI Solutions
2
More topics2 questions · ~16 min
Ensuring Microservices ConsistencyHard

Explain how to maintain data consistency across microservices, including transactions, events, retries, reconciliation, and failure handling.

distributed systemsdata consistencyarchitectureBBSRI Solutions
Star vs Snowflake for Sales AnalyticsMedium

Compare star and snowflake schemas for warehouse design, including trade-offs in normalization, query simplicity, and analytics performance.

JoinsData WranglingGroup ByBBSRI Solutions

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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
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
Data Cleaning and PrepMedium
Practice
Practice drill

Prepare valid encounter records by trimming text, excluding missing values, and removing invalid charges.

missing valuesdata cleaningData WranglingMassachusetts General HospitalCognizantDataVisor
The finish line: interview-readyComplete all 7 questions plus 3 hands-on drills to finish this plan.