Technical interview guide

ETL vs. ELT

Transform-before-load versus load-then-transform, and why modern warehouses shifted the order.

Read: 28 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: Data ArchitectData EngineerForward Deployed Engineer (FDE)

Scope: BigQuery documentation current 2026-08-31; Apache Spark 4.2; Apache Airflow stable current 2026-08-31; Apache Iceberg and Parquet current 2026-08-31; PostgreSQL 18.

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: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-03

QA-1

Choose ETL, ELT, or hybrid for a new analytics platform.

QA-2

Design a governed raw and curated data architecture.

QA-3

Build an idempotent incremental batch pipeline.

QA-4

Design a change-data-capture pipeline from a transactional database.

QA-5

Handle schema evolution without silently corrupting analytics.

QA-6

Design data quality controls for an ELT platform.

QA-7

How do you evaluate and control compute costs when deciding between dedicated Spark ETL clusters and pushing heavy transformations down into a cloud data warehouse ELT engine?

QA-8

Plan and execute a large historical backfill safely.

QA-9

Design slowly changing dimensions with late facts and corrections.

QA-10

Migrate a legacy ETL estate to cloud ELT.

QA-11

Optimize ELT warehouse cost without weakening correctness.

QA-12

Design orchestration for reliable batch transformations.

QA-13

Secure an ELT platform containing sensitive customer data.

QA-14

Implement lineage that remains useful during incidents and change.

QA-15

Design reverse ETL into customer-facing operational systems.

QA-16

Recover a pipeline after checkpoint or state corruption.

QA-17

Design multi-tenant ELT with strong isolation.

QA-18

Build a data contract between an operational producer and analytics consumers.

QA-19

Choose partitioning, clustering, and file layout for analytics data.

QA-20

Operate an ELT platform with clear service levels and incident response.

QA-21

Decide between batch, micro-batch, and continuous streaming.

QA-22

Reconcile financial data through an ETL pipeline.

QA-23

Design deletion and retention across raw and transformed data.

QA-24

Review an ETL or ELT design for failure and recovery.

QA-25