Data Warehouse and Dimensional Modeling Questions

Design and model scalable analytical data systems using dimensional modeling principles and data warehouse architecture patterns. Core concepts include fact and dimension tables, defining and enforcing grain, surrogate keys, degenerate and role playing dimensions, conformed dimensions, and handling slowly changing dimensions including Type One, Type Two, and Type Three. Understand schema choices and trade offs such as star schema versus snowflake schema, normalization versus denormalization, and fact table types including transactional, periodic snapshot, and accumulating snapshot. Apply design decisions to meet query patterns and performance goals by considering partitioning, indexing, compression, columnar storage, and aggregation strategies. Be able to design schemas for different business domains, reason about data integration and consistency, and optimize for common analytical workloads and reporting requirements.

HardTechnical

81 practiced

Leadership: As a senior data engineer, how do you decide whether to adopt a new dimensional modeling standard across teams (e.g., change surrogate key strategy or SCD handling)? Describe stakeholders you involve, decision criteria, rollout plan, and how you measure success.

MediumSystem Design

95 practiced

Design a star schema for an advertising analytics product that reports on impressions, clicks, and conversions. Specify one fact table and necessary dimensions (at minimum: date, campaign, creative, user, and device). Define the grain clearly and justify why you chose that grain.

HardTechnical

146 practiced

You need to integrate two sources of customer data with different schemas and business keys into a single conformed customer dimension. Outline a practical ETL plan covering: key reconciliation, attribute mapping, conflict resolution rules, and testing strategies to ensure consistent conformance.

MediumTechnical

84 practiced

Explain how columnar warehouses (BigQuery, Snowflake, Redshift) implement clustering/partitioning and how that differs from row-based OLTP indexing. Provide guidance for selecting clustering columns and partition keys for the common analytics query pattern: filter by date range and group by product category.

MediumTechnical

98 practiced

Create a data-quality test plan for dimension tables in your warehouse. Include checks for uniqueness of business keys, referential integrity between fact and dimension, nullability expectations, and acceptable value ranges. For each test, indicate the remediation steps you would take when a test fails.

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