5 exercises — choose the best-structured answer to common data quality engineering interview questions. Focus on validation frameworks, test architecture, data contracts, lineage, and anomaly detection.
Structure for data quality interview answers
Name the framework objects: Great Expectations has Expectations, Suites, Validators, Checkpoints — use the correct terms
Distinguish test types: schema tests vs singular tests vs custom generic tests have different scopes and reuse patterns
Cover enforcement layers: data contracts need schema, runtime, and CI enforcement — not just documentation
Address tooling specifics: dbt, OpenLineage, Soda, DataHub — name the config options and integration points
0 / 16 completed
1 / 16
The interviewer asks: "Explain how Great Expectations works — how do you define expectations, validate data, and integrate validation into a data pipeline?" Which answer best covers Great Expectations architecture?
Option B covers all six layers: core object model (Expectation, Suite, Validator, Data Context), expectation categories (column, table, multi-column) with specific method names, Data Docs HTML output, Checkpoint as the production unit with three Action types (Slack, store result, rebuild docs), Airflow integration with the exception-raising failure behaviour, and auto-profiling as a bootstrap strategy. Options A, C, D each describe the concept correctly but don't cover the Checkpoint execution model, Action types, or Airflow integration mechanics.
2 / 16
The interviewer asks: "Compare dbt singular tests and generic tests — how do you write each, and when would you use custom generic tests vs schema.yml assertions?" Which answer best covers dbt test architecture?
Option B covers all seven dimensions: schema test YAML syntax with a concrete column example, singular test pattern (0-row-returning SQL), custom generic tests with the macro pattern and parameters, dbt-utils and dbt-expectations package capabilities with specific function names, test severity (warn vs error with YAML syntax), --store-failures for failure investigation, and test selection syntax for CI. Options A, C, D each describe the two test types correctly but don't cover custom generic test macros, severity levels, store-failures, or test selection syntax.
3 / 16
The interviewer asks: "What is a data contract, how do you specify one, and how do you enforce it in a modern data stack?" Which answer best covers data contract architecture?
Option B covers all six dimensions: the four components of a contract (schema, semantics, SLA, ownership), ODCS specification format with concrete YAML fields, dbt model contracts (contract: {enforced: true}), the producer-consumer workflow with the parallel migration pattern for breaking changes, three enforcement layers (dbt materialisation, runtime GE/Soda, CI PR checks), and the contract registry pattern with tooling. Options A, C, D each identify the concept correctly but don't cover specification formats, the migration workflow, CI enforcement, or the contract registry pattern.
4 / 16
The interviewer asks: "Explain column-level lineage — how is it different from table-level lineage, why does it matter for impact analysis, and how do modern tools capture it?" Which answer best covers data lineage depth?
Option B provides the complete picture: the precise difference between table and column lineage with concrete column-path examples, a quantified impact scale (50-200 downstream columns across 30+ models), lineage capture mechanisms (SQL parsing with specific tools: dbt manifest, Marquez, OpenLineage, DataHub, SQLGlot), the ColumnLineageDatasetFacet spec, the SELECT * lineage-breaking problem with the best-practice fix, the dbt + Airflow + DataHub integration stack, and freshness SLA propagation as an advanced use case. Options A, C, D each identify the use case correctly but provide no capture mechanisms, tooling specifics, or the SELECT * problem.
5 / 16
The interviewer asks: "Compare Z-score, IQR, and ML-based approaches to detecting data quality anomalies in a pipeline — when would you use each?" Which answer best covers data anomaly detection?
Option B provides mechanical definitions of all three approaches (Z-score formula, IQR Tukey fence formula), specific limitations (Z-score masking effect from outlier-inflated σ, IQR seasonal false positives), ML algorithm options beyond just Prophet (ARIMA, Isolation Forest for multivariate, LSTM autoencoder), data requirements (2-4 weeks minimum), a layered practical framework (Z-score/IQR for volume, ML for KPIs), alert fatigue warning, and specific tooling (Soda Cloud, Monte Carlo, dbt-utils.recency, custom Prophet on information_schema). Options A, C, D each name the right algorithms but don't explain the masking effect, seasonal false positives, multivariate options, or tooling.
6 / 16
Sarah from the Data Quality team sends you this Slack message: 'Hey, I'm seeing a lot of NULL values in the `customer_email` column after the recent ETL run. The dashboard is flashing red! Can you investigate?' Which approach would be MOST effective for quickly understanding the scope and potential root cause of this issue?
Sarah's message indicates an immediate problem. Great Expectations is ideal here because it can automatically profile data and highlight anomalies – in this case, a sudden influx of NULL values – without requiring you to manually write complex queries or delve into log files. Option A is too broad; option B focuses on the *right* tool for the job, while options C and D are reactive responses that don't address the core issue.
7 / 16
Mark, a senior engineer, adds this comment to your PR describing a new data validation step: 'Ensure all numeric fields in the `orders` table have a minimum value of 0. This prevents negative order amounts.' What type of test is Mark implicitly requesting?
Mark's comment highlights the importance of enforcing business rules. A 'business rule validation test' correctly describes a scenario where you are checking if data adheres to predefined constraints (in this case, non-negative values). Schema constraint tests focus on data types; transformation tests handle data changes; and outlier detection is for identifying unusual values.
8 / 16
During a standup meeting, David says: 'We're implementing data contracts to manage the quality of our customer data. We'll define expected schemas and validation rules within dbt.' What is David primarily referring to?
Data contracts are a key element in modern data quality management. They represent a structured approach to defining and enforcing data standards across an organization's data landscape, ensuring consistency and reliability. While dbt can certainly *implement* parts of this process (specifically tests), David's statement refers to the broader concept of data contract architecture.
9 / 16
Sarah from the Data Quality team sends you this Slack message: 'Hey, I'm seeing a lot of NULL values in the `customer_email` column after the recent ETL run. The dashboard is flashing red! Can you investigate?' Which approach would be MOST effective for quickly understanding the scope and potential root cause of this issue?
Sarah's message indicates an immediate problem. Great Expectations is ideal here because it can automatically profile data and highlight anomalies – in this case, a sudden influx of NULL values – without requiring you to manually write complex queries or delve into log files. Option A is too broad; option B focuses on the *right* tool for the job, while options C and D are reactive responses that don't address the core issue.
10 / 16
Mark, a senior engineer, adds this comment to your PR describing a new data validation step: 'Ensure all numeric fields in the `orders` table have a minimum value of 0. This prevents negative order amounts.' What type of test is Mark implicitly requesting?
Mark's comment highlights the importance of enforcing business rules. A 'business rule validation test' correctly describes a scenario where you are checking if data adheres to predefined constraints (in this case, non-negative values). Schema constraint tests focus on data types; transformation tests handle data changes; and outlier detection is for identifying unusual values.
11 / 16
During a standup meeting, David says: 'We're implementing data contracts to manage the quality of our customer data. We'll define expected schemas and validation rules within dbt.' What is David primarily referring to?
Data contracts are a key element in modern data quality management. They represent a structured approach to defining and enforcing data standards across an organization's data landscape, ensuring consistency and reliability. While dbt can certainly *implement* parts of this process (specifically tests), David's statement refers to the broader concept of data contract architecture.
12 / 16
Maria from the Data Governance team asks you during a code review: 'I noticed this PR adds a new column, `user_age`, but it doesn't specify its data type. How does that impact our overall data quality strategy?' Which of the following best describes Maria's concern?
Maria is concerned about the lack of explicit data type definition. Without a clearly defined schema (e.g., integer, string), subsequent data validation and transformation steps will be unreliable. The absence of this information introduces ambiguity and increases the risk of introducing incorrect data values into the system.
13 / 16
During a Slack conversation with the BI team, Alex mentions: 'We're using Great Expectations to proactively monitor our sales data. We define 'expectations' around things like unique customer IDs and revenue totals, then run validation checks against our incoming datasets.' What core principle is Alex demonstrating?
Alex is illustrating the concept of *proactive* data quality monitoring using Great Expectations. Expectations define desired data characteristics and serve as a basis for automated validation. This contrasts with reactive approaches that only address issues after they've been detected.
14 / 16
You're reviewing a PR where a new data quality rule is implemented in dbt. The description reads: 'This test ensures that the `product_price` column always has a value greater than zero.' What type of test is this primarily?
This is an example of a *schema assertion* test within dbt. Schema assertions directly verify that column definitions (like data types and constraints) adhere to specified rules. These tests are typically declarative – focusing on *what* the data should be rather than *how* it's validated.
15 / 16
During a standup meeting, Ben says: 'We're using lineage tracking to understand how changes in the source system impact downstream reports. It's crucial for identifying potential data quality issues when we update our customer database.' What is Ben primarily discussing?
Ben is referring to *data lineage*, specifically focusing on impact analysis. Understanding the flow of data from its source through various transformations and reports allows teams to proactively identify potential issues caused by upstream changes. This helps prevent cascading data quality problems.
16 / 16
Sarah sends a Slack message: 'The `customer_address` column is showing inconsistent formatting – some addresses have commas, others don't. We need to standardize it.' Which approach would be MOST effective for addressing this data quality issue?
Standardizing the `customer_address` column is a direct response to inconsistent formatting. This can be achieved through automated transformations within a data pipeline or manual cleaning steps. The key is to establish and enforce a single, consistent format for all addresses.
What does "Data Quality Engineer — Interview Questions — Best-Answer Practice" cover?
Practice answering Data Quality Engineer interview questions in professional English. 5 exercises on Great Expectations, dbt test types, data contracts, column-level lineage, and anomaly detection.
How many questions are in this interview set?
This set has 16 exercises, each with a full explanation.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to use with no account, sign-up, or paywall.
Do these exercises include model answers?
Yes. Each interview question gives you several possible responses and asks you to pick the one that communicates most clearly and completely — the explanation then breaks down exactly why that answer works, including the specific vocabulary a strong candidate would use.
What if I choose an answer that isn't the strongest one?
You'll see which option was correct and read a full explanation of why it's stronger than the alternatives, plus the key vocabulary and phrasing worth reusing in a real interview.
Can I retry the questions?
Yes — use the "Try again" button on the results screen to reset and go through the set again.
Is this the same as a real technical or behavioural interview?
No — it's focused practice for the language side of interviewing: recognising which phrasing sounds precise and confident versus vague, and knowing the vocabulary interviewers expect for this role. It won't replace mock interviews, but it builds the vocabulary you'll need in one.
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Every question is written by the CoderSlingo team based on real technical interview patterns for this role, then reviewed for accuracy and clarity.