Data Quality & ETL QA Interview Prep
Data QA interviews test whether you can guarantee that data is correct as it moves and transforms across pipelines. Expect questions on ETL validation, SQL-based checks, schema evolution, reconciliation between source and target, and catching silent data corruption at scale.
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What to expect.
Expect heavy SQL: writing queries to compare source and target, detect duplicates, check referential integrity, and profile distributions. You will be asked how you validate an ETL or ELT pipeline end to end, how you test transformations and aggregations for correctness, how you handle schema evolution and backfills without corrupting history, and how you catch data drift. Interviewers value reconciliation thinking (does the target reconcile to the source?) and an understanding of testing big-data and streaming pipelines where exact-match assertions break down.
Key interview topics.
Core areas interviewers evaluate for Data Quality / ETL QA Engineer roles.
ETL / Pipeline Validation
Validating extract, transform, and load steps end to end, including transformations, joins, and aggregations.
SQL & Data Profiling
Source-to-target comparison queries, duplicate and null checks, referential integrity, and distribution profiling.
Data Quality Checks
Completeness, accuracy, consistency, uniqueness, and timeliness checks, and automating them as data tests.
Schema & Contracts
Schema evolution, backfills without corrupting history, and data contracts between producers and consumers.
Reconciliation
Proving the target reconciles to the source by counts, sums, and key-level diffs after each run.
Big Data & Streaming
Testing Spark and streaming pipelines where exact-match assertions give way to tolerance and idempotency checks.
Sample Interview Questions
Questions based on real Data Quality / ETL QA Engineerinterview patterns. Practice answering these with AssertHired’s AI interviewer.
- 01
How would you validate an ETL pipeline end to end from source to target?
- 02
Write a SQL approach to detect duplicate or missing records between a source and a target table.
- 03
How do you test a transformation that aggregates millions of rows for correctness?
- 04
How would you catch silent data corruption introduced by a schema change or a backfill?
- 05
What data quality dimensions do you check, and how do you automate them?
- 06
How do you reconcile a target dataset to its source after a pipeline run?
- 07
How would you test a streaming pipeline for exactly-once processing and idempotency?
Who This Prep Is For
This prep is for data quality engineers, ETL testers, and SDETs who test data pipelines and warehouses. If your interviews cover SQL-heavy validation, ETL correctness, reconciliation, and big-data testing, this track matches what you will encounter.
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Frequently Asked Questions
How much SQL do I need for a data QA interview?
A lot. Data QA is SQL-heavy: you will write queries to compare source and target, find duplicates, check integrity, and profile data. Comfort with joins, window functions, and aggregations is expected.
How is data/ETL testing different from application testing?
Application testing checks behavior and UI; data testing checks that data is correct as it moves and transforms. The focus is on completeness, accuracy, reconciliation, schema evolution, and catching silent corruption across large volumes.
What tools come up in data QA interviews?
SQL above all, plus warehouse and pipeline tools (dbt, Spark, Airflow), data-quality frameworks (Great Expectations, Deequ), and sometimes streaming systems like Kafka. The concepts matter more than any single tool.
Can I practice data QA questions on AssertHired?
Yes. The AI interviewer asks ETL validation, SQL data-check, and reconciliation questions with follow-ups and scores you across four dimensions.
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