Analytics Engineer Interview Prep Guide

Start by identifying the grain of each fact table: order line items, returns, and customer events. Design dimension tables for customers, products, dates, and sales channels with appropriate slowly changing dimension strategies. Discuss denormalization tradeoffs, conformed dimensions shared across fact tables, and how you handle late-arriving data. Show that your model optimizes for the analytical queries stakeholders will actually run.

Discuss the staging-intermediate-marts layering pattern. Staging models map 1:1 to source tables with cleaning and type casting. Intermediate models handle complex transformations and business logic. Mart models are consumption-ready for specific domains. Cover naming conventions, model materialization strategies (view vs table vs incremental), testing at each layer, and documentation with dbt docs. Mention how you manage cross-domain dependencies.

Use date truncation to month level, then self-join or use window functions to identify consecutive month sequences. A LAG window function across ordered months per customer can check for consecutive month gaps. Alternatively, use GENERATE_SERIES or a date spine to identify expected months and LEFT JOIN to find missing ones. Discuss how you would handle edge cases like timezone differences in purchase timestamps.

Walk through the discovery (how you noticed the issue), the investigation (root cause analysis), the fix (both immediate and long-term), and the impact (what decisions were affected and how you communicated the correction to stakeholders). Show that you implement preventive measures like data tests and monitoring to catch similar issues proactively. Quantify the business impact of the incorrect data.

Cover multiple testing layers: schema tests (not null, unique, accepted values, relationships), custom SQL tests for business logic validation, freshness checks on source data, and row count anomaly detection. Discuss testing in CI/CD pipelines before merging changes, testing strategies for incremental models, and how you balance test coverage with pipeline runtime. Mention dbt expectations or elementary for advanced data quality monitoring.

Trace both metrics back to their source definitions. Compare the SQL logic, filter conditions, date ranges, and source tables. Common causes: different metric definitions (gross vs net revenue), different data sources (real-time vs batch), inclusion and exclusion criteria, and timezone handling. After identifying the discrepancy, propose a single source of truth through a shared semantic layer or metric store. Show that you treat metric consistency as a systemic problem, not a one-off investigation.

Star schema provides clear separation of facts and dimensions, easier maintenance, and better storage efficiency but requires joins at query time. OBT pre-joins everything for maximum query simplicity and performance but creates data redundancy, longer build times, and maintenance challenges. Discuss when each is appropriate: OBT for simple analytics teams with straightforward needs, star schema for complex analysis with multiple grain levels. Mention the modern trend of wide tables in column-store warehouses where join cost is less of a concern.

Discuss specific improvements: implementing version control and PR review for SQL, setting up CI/CD for dbt models, creating development and staging environments, standardizing coding conventions, building documentation and data catalogs, and establishing data governance processes. Quantify the impact: reduced errors, faster onboarding for new analysts, and improved stakeholder trust in data quality.