Data Engineering Interview Questions: a complete prep guide

Data Engineering Interview Questions: a complete prep guide

Data engineering interviews are hard to prepare for because the field is so wide: one loop can touch SQL, Python, data modelling, pipeline design, distributed processing and cloud warehousing. Grind a single skill — fifty SQL problems, say — and you walk in strong on one round and exposed on the other four. The candidates who get offers prepare across the whole surface and, more importantly, practise the one habit that cuts through every round: not just producing the right answer, but explaining why it is right, where it would break, and what they would ask before committing.

This is a complete guide to the data engineering interview questions you will actually face in 2026. It maps the full loop, breaks down the skill areas and the questions each one tests, goes deep on the SQL and modelling that come up most, and ends on the difference between a hire and a no-hire — which, according to engineers who run these loops, is almost never the answer itself.

The data engineering interview loop

A typical data engineering loop is a behavioural round plus four technical rounds, with senior loops adding big-data and system-design-for-data.

Round	What it covers
Behavioural	Ownership, collaboration, handling ambiguity and incidents
SQL	Analytical queries against a real schema, under time
Python	Data wrangling and scripting, not algorithms
Data modelling	Designing a schema for a business scenario
Pipeline / architecture design	How data is ingested, transformed, stored and served
Big data / system design (senior)	Spark, scale, cost-vs-latency-vs-correctness trade-offs

Company screens compress this — Meta runs a combined SQL-and-Python tech screen, DoorDash a SQL-heavy phone screen, Netflix a hard SQL set — and the per-company breakdowns live on the Zynter blog.

The skill areas and what each tests

Area	What it tests	Example topics
SQL	Correct, efficient analytical queries	Window functions, CTEs, joins & self-joins, dedup, optimisation, ACID, normalisation
Python	Data wrangling with the standard library	Dicts/lists/sets, generators (yield), decorators, pandas vs PySpark, retries/backoff
Data modelling	Turning a business need into a schema	Star vs snowflake, SCD types, grain, factless facts, surrogate keys
Pipelines & ETL	Reliable, orchestrated data flow	ETL vs ELT, Airflow DAGs, CDC, idempotent backfills, failure handling, SLAs
Big data / Spark	Processing at scale	RDD vs DataFrame, shuffles, broadcast joins, data skew, repartition vs coalesce
Warehousing & quality	Trustworthy, governed data	Snowflake/BigQuery, dbt tests, lakehouse, PII handling, data contracts, observability

Each of these has a focused deep-dive on the Zynter blog — the company DE screens, data modelling, and PySpark among them — so use this table to find your weak area and drill it.

SQL: the questions that come up most

SQL is the non-negotiable round — every data engineering loop has at least one, and it is usually the gate. The recurring topics:

• Window functions. Ranking and running calculations are the single most-tested shape. Know the difference cold: ROW_NUMBER() gives every row a unique number; RANK() ties then skips (1, 2, 2, 4); DENSE_RANK() ties without skipping (1, 2, 2, 3).
• CTEs for readable multi-step queries instead of nested subqueries.
• Deduplication — and the edge case that separates juniors from seniors. SELECT DISTINCT region FROM nodes is correct, but if region can be NULL, DISTINCT returns NULL as a value; flag that and ask whether the business wants it included.

-- window function: first event per user (the shape the SQL round loves)
SELECT user_id, event_date
FROM (
  SELECT user_id, event_date,
         ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_date) AS rn
  FROM events
) t
WHERE rn = 1;

• Optimisation — read the execution plan, add the missing index, filter before the join, avoid SELECT *.
• Fundamentals — ACID, normalisation (1NF/2NF/3NF), UNION vs UNION ALL, upsert/MERGE.

Data modelling and pipelines

Data modelling questions ask you to turn a business scenario into a schema. Know star vs snowflake (default to star), the grain decision (state what one fact row represents before anything else), the SCD types (Type 2 is the default for history), and surrogate vs natural keys. The full treatment is in the data modelling guide on the blog.

Pipeline questions test reliability. Know ETL vs ELT (transform before loading vs load raw and transform in the warehouse — ELT scales better on cloud compute), the Airflow DAG (directed, acyclic — tasks never loop back), idempotent backfills for reprocessing history, change data capture for keeping a warehouse in sync without full reloads, and how you handle failures (retries with backoff, dead-letter queues, alerting). Senior rounds push on the architecture: given six sources and a dashboard-by-8am SLA, start with the constraints, not the boxes.

What actually separates a hire from a no-hire

Here is the thing engineers who run these loops say most consistently: the hire/no-hire line is almost never whether you got the right answer. It is whether you got the right answer and could explain why it was right, where it might break, and what you would ask before committing. Most prep platforms hand you a clean prompt, an expected output, and a green checkmark — which trains you to produce answers. The interview is grading something broader.

The habit that demonstrates it is a four-step method you can run on any question:

1. Restate the problem in your own words before writing anything.
2. Trace a concrete example by hand to confirm you understood it.
3. Build the solution with your reasoning narrated out loud — why this join, why this grain, why a window function over a subquery.
4. Raise the edge cases you did not handle and say whether they need handling — the NULL in the dedup, the late-arriving row, the duplicate at a different grain.

Those four moves map directly onto the four dimensions a scored round grades — problem solving (the right approach), communication (narrating and justifying it), technical execution (clean, correct SQL or Python), and time management (reaching a defensible answer in the window). A green checkmark on a practice platform trains none of them, which is exactly the gap between knowing the answers and getting hired.

Data engineering
Company-agnostic · SQL + Python screen · 60 min
Run a timed SQL and Python screen out loud, narrating the why

Practise on Zynter →

How to prepare for a data engineering interview

• Prepare across all the areas, not just SQL. Use the skill map to find your weakest and drill it with the focused guide on the blog.
• Practise SQL and Python on a timer, since the screens are fast and pace is a common failure point.
• Drill the four-step method — restate, trace, narrate, raise edge cases — on every problem until it is automatic.
• Model out loud. For data modelling, talk through grain and dimensions as if to an interviewer.
• Finish with full mocks where someone pushes back, because the broader thing the interview grades only shows up under follow-ups.

If you want raw, timed volume across SQL, Python and PySpark first, the sister platform PipeCode is built for exactly that kind of data-engineering practice; use it to build speed and coverage, then come back here to rehearse explaining your answers out loud.

Data engineering
Company-agnostic · Data modelling · 30 min
Defend a data model and a pipeline choice under follow-ups

Practise on Zynter →

Frequently asked questions

What does the data engineering interview process look like?

A typical loop is a behavioural round plus four technical rounds — SQL, Python, data modelling and pipeline or architecture design — often starting with a timed phone screen that combines SQL and Python. Senior loops add a big-data or system-design-for-data round. Company screens vary: some run SQL and Python together in one hour, others split them, and the harder companies lean heavily on SQL. The common thread is breadth, which is why preparing one skill in isolation leaves you exposed.

What topics should I study for a data engineering interview?

Cover six areas: SQL (window functions, CTEs, joins, optimisation), Python (data wrangling, generators, pandas vs PySpark), data modelling (star schema, slowly changing dimensions, grain), pipelines and ETL (ETL vs ELT, Airflow, change data capture, idempotent backfills), big data with Spark (shuffles, broadcast joins, skew), and warehousing and quality (dbt, data contracts, governance, PII). Identify your weakest area and drill it, because the interview will probe all of them, not just your strongest.

How important is SQL in a data engineering interview?

It is the most important single skill — essentially every data engineering loop includes at least one heavy SQL round, and it is frequently the gate that must be cleared before anything else. Interviewers expect fluent window functions, clean CTEs, correct joins, and an instinct for optimisation and edge cases like nulls in a deduplication. Because it is both the most common and the most time-pressured round, it should be the first thing you make automatic.

What's the difference between ETL and ELT?

In ETL, data is transformed in a separate processing engine before it is loaded into the warehouse. In ELT, raw data is loaded into the cloud warehouse first and transformed there using the warehouse's own compute, which scales better and is the modern default with tools like dbt. The interview answer ties the choice to context: ELT when you have elastic cloud compute and want flexibility, ETL when transformation must happen before landing for compliance or when the target cannot transform efficiently.

How is a senior data engineering interview different?

Seniors get the same SQL and Python rounds but are weighted far more on design and judgement: data modelling with clear grain and trade-offs, pipeline architecture starting from constraints (SLAs, sources, security) rather than boxes, and reasoning about cost versus latency versus correctness at scale. You are also expected to raise edge cases and failure modes unprompted and to discuss data quality, governance and observability. The bar shifts from "can you write it" to "can you design and defend it".

How do I practise data engineering interviews out loud?

Solving problems silently against a green checkmark will not prepare you for the follow-ups that decide the round. On Zynter, the Data engineering track runs SQL, Python and data-modelling rounds against real schemas, with a voice interviewer that pushes on your choices — "why that grain?", "what breaks if this column is null?" — and a debrief on problem solving, communication, technical execution and time management. It is a genuine data-engineer mock interview on demand, and the score is a practice signal, not a hiring decision.

Practise it on Zynter

The right query isn't what gets you hired. Explaining why, where it breaks, and what you'd ask — out loud — is.

Run SQL, Python and data-modelling rounds against real schemas, and talk through every choice with a voice interviewer that keeps asking "why?" and "what breaks?" — then read the debrief. Zynter.ai is on-demand mock interviews modelled on real company loops, with 150+ rounds across 20 companies (Sep 2026), including Meta, DoorDash, Airbnb and Netflix data-engineering sets — about $8–$12 for a full 60-minute mock, and two free credits to start.

Practise a data engineering round →
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