What data architect interviews cover
Data architect interviews assess the ability to design scalable data platforms that meet analytical and operational needs, govern data quality and access, and align data infrastructure decisions with business requirements. The role sits at the intersection of data engineering (the how of building pipelines and storage systems), data strategy (the what and why of data collection and use), and governance (the policies around data access, quality, and compliance). Interview questions span all three dimensions.
Data architect roles are found at organisations with significant data complexity: banks, insurers, retailers, large platform technology companies, and consulting firms that design data platforms for clients. The technical depth expected is high: candidates who cannot explain the differences between data warehouse, data lake, and data lakehouse architectures in concrete terms, or who cannot discuss data modelling approaches, are quickly eliminated at the technical screening stage.
Technical data architect interview questions
"When would you choose a data warehouse versus a data lake?" Data warehouses (Snowflake, BigQuery, Redshift) excel for structured, well-defined reporting and analytics where query performance and data quality are paramount. Data lakes (S3, ADLS, GCS with Spark or similar) suit unstructured or semi-structured data, raw data storage for ML use cases, and scenarios where schema-on-read flexibility is valued. Data lakehouses (Databricks Delta Lake, Apache Iceberg, Apache Hudi) attempt to combine the storage economics of a data lake with the ACID transactions, data quality, and query performance of a warehouse. The right answer depends on use cases, team capability, and existing stack.
"How do you design a data model for a given business domain?" Describe your process: understand the business entities and their relationships, identify the query patterns (analytical or operational?), choose between dimensional modelling (star or snowflake schema for analytical use cases) or normalised relational modelling (for transactional use cases), define primary and foreign keys, and document the model in a way that data consumers can use. Strong candidates distinguish between logical data models (what entities and relationships exist in the business) and physical data models (how they are implemented in the specific technology chosen).
"How do you approach data quality governance?" Data quality governance involves: defining data quality standards (completeness, accuracy, consistency, timeliness), implementing automated quality checks in the pipeline (Great Expectations, dbt tests, or native data catalogue tools), establishing data stewardship (who is accountable for quality in each domain), and creating visibility into quality metrics for stakeholders. For regulated industries, connect data quality to audit trail requirements, GDPR data accuracy obligations, and regulatory reporting accuracy.