Data Transformation and Loading Questions

Focuses on the extract transform load and extract load transform approaches for ingesting transforming and loading data. Candidates should understand three core stages: extract which is acquiring data from sources such as application programming interfaces databases logs and message queues; transform which is cleaning validating reshaping aggregating and enriching data to meet downstream requirements; and load which is writing processed data to targets such as analytic databases data warehouses data lakes or reporting systems. Topics include the differences between extract transform load and extract load transform, incremental loads versus full refresh, scheduling and orchestration best practices, tooling and frameworks used for transformation and orchestration, idempotency and deduplication strategies, error handling and retry semantics, data quality checks end to end validation recovery and integration with business intelligence and analytics consumers. Interview focus is on concrete transformation logic pipeline orchestration and validation strategies and on choosing the right pattern and tooling for given constraints.

MediumTechnical

60 practiced

You have a staging table `stg_orders` and a target table `orders`. Schemas:

sql

-- staging
stg_orders(order_id, customer_id, amount, status, last_updated)
-- target
orders(order_id PRIMARY KEY, customer_id, amount, status, last_updated, deleted_flag)

Write an ANSI MERGE statement (compatible with Snowflake/BigQuery) to upsert staging into target: update changed columns, insert new rows, and mark deleted rows by setting deleted_flag when staging indicates cancellation. Explain how to make the operation idempotent and efficient for nightly runs.

MediumTechnical

44 practiced

Propose a testing strategy for SQL-based transformations that covers unit tests, integration tests, and regression tests. Provide examples of each (record-level fixtures, aggregate assertions, null-handling tests), explain how to automate tests in CI for pull requests, and how to manage test data and fixtures for reproducible runs.

EasyTechnical

49 practiced

Explain idempotency and deduplication strategies you would apply to an incremental load so that retries or replays don't corrupt metrics. Describe concrete techniques (staging tables + swap, MERGE with dedupe keys, job-run identifiers, checksums, watermarking) and discuss pros/cons from an analyst's perspective.

EasyTechnical

48 practiced

You are responsible for the daily sales dashboard. List a set of data quality checks you would run each day before publishing the dashboard. For each check describe the SQL logic, threshold (example), and the intended remediation or escalation. Include checks such as row count deltas, null-rate thresholds, range checks (amounts not negative), and high-level distribution tests.

HardTechnical

49 practiced

Implement sessionization logic to compute user sessions (session_id, start_ts, end_ts, duration) using event timestamps with a 30-minute inactivity gap. Also account for late events allowed up to 2 hours late: describe how you would update existing sessions when late events arrive (both in SQL and in a stream processor), how to persist sessions, and how to emit correction records for downstream metrics.

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