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Technical interview guide

ETL for Reporting

The lighter-weight, reporting-focused data prep that sits between raw warehouse tables and a BI tool.

Read
45 min
Practice MCQs
25
Interview QA
25
Edition
v4
Editorial status
Reviewed

Scope: Current dbt, Google Dataform and BigQuery, Snowflake streams/tasks, Apache Airflow, Apache Iceberg, and OpenLineage guidance reviewed 2026-09-04.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: · Written: · Reviewed:

QA-1

Design a report-serving model from dashboard requirements.

QA-2

Implement an idempotent daily reporting load.

QA-3

Design an incremental extraction watermark.

QA-4

Validate incremental equivalence over time.

QA-5

Choose keys and deduplication for a report fact.

QA-6

Handle deletes in incremental reporting ETL.

QA-7

Build a Type 2 dimension incrementally.

QA-8

Plan a large historical backfill.

QA-9

Design scheduling and concurrency for reporting pipelines.

QA-10

Design atomic publication for reporting datasets.

QA-11

Evaluate a wide reporting table versus a star schema.

QA-12

Build and validate a daily reporting aggregate.

QA-13

Design lateness and period-close handling.

QA-14

Handle partial input and quarantine decisions.

QA-15

Design schema-evolution handling for reporting ETL.

QA-16

Build layered tests for a reporting pipeline.

QA-17

Capture useful run metadata and lineage.

QA-18

Use change data capture for reporting models.

QA-19

Design failure and rollback behavior for reporting ETL.

QA-20

Secure a reporting ETL and serving layer.

QA-21

Orchestrate dependent reporting models.

QA-22

Choose append, merge, delete-insert, or partition replacement.

QA-23

Define freshness and completeness SLOs for reporting.

QA-24

Define reporting ETL reliability and cost metrics.

QA-25

Review a reporting ETL pipeline before launch.