What the junior data engineer interview looks like

Junior data engineer interviews test SQL and Python proficiency, understanding of data pipelines and ETL concepts, and your ability to think about data quality and reliability. Most processes take 2–3 weeks across 3–4 rounds. Here’s what each stage looks like and what they’re testing.

  • Recruiter screen
    30 minutes. Background overview, experience with data tools, and salary expectations. They’re confirming basic qualifications and interest in data engineering specifically (not data science or analytics).
  • Technical phone screen
    45–60 minutes. Live coding focused on SQL and Python. Expect a moderately complex SQL query (joins, aggregations, window functions) and a Python data manipulation problem (parsing, transforming, or loading data).
  • Onsite (virtual or in-person)
    3–4 hours across 2–3 sessions. Typically includes a SQL/Python coding round, a data pipeline design discussion, and a behavioral round. Some companies add a take-home exercise involving building a small ETL job.
  • Hiring manager conversation
    30 minutes. Team fit, career interests, and how you approach data quality problems. Often the final step before a decision.

Technical questions you should expect

Data engineering interviews focus on building reliable data systems, not just querying data. You’ll need to demonstrate strong SQL, working Python skills, and an understanding of how data moves from source systems to analytics-ready tables.

Write a SQL query that identifies duplicate records in a table and keeps only the most recent one.
Tests your SQL proficiency and understanding of data quality — deduplication is a daily task for data engineers.
Use a window function: ROW_NUMBER() OVER (PARTITION BY duplicate_key ORDER BY updated_at DESC) to assign a row number within each group of duplicates, ordered by recency. Wrap it in a CTE and filter where row_num = 1 to keep only the latest record. For the delete operation, you can use a CTE with DELETE where row_num > 1. Mention that in production, you’d want to log the duplicates being removed and understand why they exist before just deleting them — duplicates often signal an upstream data quality problem.
Explain the difference between a batch ETL pipeline and a streaming pipeline. When would you use each?
Tests your understanding of data architecture fundamentals, not just coding ability.
Batch ETL processes data on a schedule (hourly, daily) — it extracts data from source systems, transforms it, and loads it into a warehouse. Good for analytics workloads where data freshness of hours is acceptable. Streaming pipelines process data continuously as it arrives (using tools like Kafka, Flink, or Spark Streaming). Good for real-time dashboards, fraud detection, or any use case where latency matters. Tradeoffs: batch is simpler to build, test, and debug; streaming is more complex but provides fresher data. Most companies start with batch and add streaming only for use cases that genuinely require it.
Write a Python function that reads a CSV file, cleans the data, and writes it to a new file.
Tests practical Python skills for data manipulation — the bread and butter of junior data engineering.
Use pandas: read with pd.read_csv(), then apply cleaning steps: drop duplicates (df.drop_duplicates()), handle missing values (df.dropna() or df.fillna() depending on context), strip whitespace from string columns (df[col].str.strip()), enforce data types (convert dates, ensure numeric columns are numeric), and filter invalid rows. Write to output with df.to_csv(). Mention logging: print how many rows were read, how many were dropped and why, and how many were written. In production, you’d add error handling for malformed rows and use a schema validation library like Pydantic or Great Expectations.
What is a star schema, and why is it used in data warehousing?
Tests your understanding of data modeling for analytics — foundational data engineering knowledge.
A star schema organizes data into a central fact table (containing measurable events like transactions, clicks, or orders) surrounded by dimension tables (descriptive context like customer, product, date, and location). It’s called a “star” because the diagram looks like a star with the fact table in the center. Benefits: simple queries (analysts can join fact to dimension with straightforward joins), fast aggregation performance (denormalized dimensions avoid complex joins), and intuitive structure (business users can understand it). Compare with a snowflake schema, where dimensions are normalized into sub-dimensions — more storage-efficient but harder to query.
How would you design a simple data pipeline that ingests data from a REST API daily and loads it into a data warehouse?
Pipeline design question that tests whether you can think about the full data lifecycle.
Step 1: Extract — write a Python script that calls the API with pagination, handles rate limits and retries, and saves raw JSON responses to cloud storage (S3 or GCS) as a landing zone. Step 2: Transform — parse the JSON, flatten nested structures, apply data type conversions, validate against expected schema, and handle missing or malformed records. Step 3: Load — write the cleaned data to the warehouse (BigQuery, Snowflake, or Redshift) using an append or upsert strategy. Orchestrate with a scheduler (Airflow or a cron job). Add monitoring: alert if the pipeline fails, if row counts drop unexpectedly, or if the API returns errors. Mention idempotency — the pipeline should produce the same result if run twice for the same date.
What is data partitioning, and why does it matter for query performance?
Tests understanding of how data storage affects performance at scale.
Partitioning divides a large table into smaller, more manageable segments based on a column value (typically date). When you query with a filter on the partition column (e.g., WHERE date = '2026-01-15'), the database only scans the relevant partition instead of the entire table. This dramatically reduces query time and cost, especially for large tables with billions of rows. Common strategies: range partitioning (by date), hash partitioning (by user ID for even distribution), and list partitioning (by region or category). Mention that in cloud warehouses like BigQuery, partitioning by date is almost always a best practice for event-level tables.

Behavioral and situational questions

Behavioral questions for data engineering roles focus on how you handle data quality issues, work with downstream consumers (analysts, data scientists), and approach reliability and testing. Use the STAR method (Situation, Task, Action, Result) for every answer.

Tell me about a time you found and fixed a data quality issue.
What they’re testing: Attention to detail, understanding of data quality, ability to investigate and resolve issues.
Use STAR. Describe the Situation (how you discovered the issue — maybe a metric looked wrong or a downstream consumer flagged it), your Task (trace the root cause and fix it), the Action (how you investigated: checked source data, reviewed transformation logic, compared expected vs. actual values), and the Result (the fix, plus any preventive measures you added like data validation checks or monitoring). Show that you traced the issue to its source rather than just patching the symptom.
Describe a time you had to learn a new tool or technology to complete a project.
What they’re testing: Learning agility, resourcefulness, ability to deliver despite unfamiliarity.
Pick a real example where the learning curve was meaningful — not just reading one tutorial. Describe why the tool was needed, how you learned it (official docs, building a prototype, pair programming with someone experienced), and the outcome (you delivered the project successfully). Quantify if possible: “I went from no Airflow experience to building a production DAG with 15 tasks in 2 weeks.” Show that you have a reliable process for ramping up on new technologies.
Tell me about a time you had to work with a stakeholder who had unclear requirements.
What they’re testing: Communication skills, ability to work through ambiguity, stakeholder management.
Describe the ambiguity (e.g., an analyst asked for “clean data” without specifying what “clean” meant). Explain how you clarified the requirements: asked specific questions, showed examples, proposed a schema or sample output for approval before building the full pipeline. Show that you documented the agreed-upon requirements so there was a shared understanding. The Result should demonstrate that you delivered what they actually needed, not just what they initially asked for.
Give an example of when you automated a manual or repetitive process.
What they’re testing: Initiative, engineering mindset, ability to identify and eliminate toil.
Describe the manual process (what it involved, how often it happened, how long it took). Explain how you identified it as automation-worthy (not just a pet project, but a genuine time drain), what you built (a script, a scheduled job, a pipeline), and the measurable impact (hours saved per week, errors eliminated, people freed up for higher-value work). Even small automations count — a Python script that formats a weekly report saves real time.

How to prepare (a 2-week plan)

Week 1: Build your technical foundation

  • Days 1–2: Review SQL beyond basics: window functions (ROW_NUMBER, RANK, LAG, LEAD, running totals), CTEs, subqueries, and CASE expressions. Practice on LeetCode (SQL section), StrataScratch, or DataLemur.
  • Days 3–4: Review Python for data engineering: file I/O, JSON/CSV parsing, pandas basics (read, filter, group, write), error handling (try/except), and working with APIs (the requests library). Build a small script that fetches data from a public API and writes it to a CSV.
  • Days 5–6: Study data engineering concepts: ETL vs. ELT, batch vs. streaming, data warehouses vs. data lakes, star schemas, slowly changing dimensions, and idempotent pipelines. Read 2–3 articles about Airflow, dbt, or Spark to understand the modern data stack.
  • Day 7: Rest. Review your notes casually but don’t cram.

Week 2: Simulate and refine

  • Days 8–9: Practice pipeline design questions out loud. Given a scenario (“ingest data from 3 APIs and build a dashboard-ready table”), walk through extraction, transformation, loading, scheduling, and monitoring.
  • Days 10–11: Prepare 4–5 STAR stories: a data quality fix, learning a new tool, automating a process, working with unclear requirements, and a challenging debugging experience.
  • Days 12–13: Research the specific company. Understand their data stack (check the job posting and engineering blog), what data sources they work with, and what their data team structure looks like. Prepare 3–4 questions about their pipeline infrastructure and data quality practices.
  • Day 14: Light review. Skim your notes, do 2–3 SQL problems, and get a good night’s sleep.

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What interviewers are actually evaluating

Junior data engineer interviews evaluate a blend of technical skills, engineering rigor, and growth potential. Here’s what interviewers are looking for.

  • SQL proficiency: SQL is the most-used tool in data engineering. Can you write correct, efficient queries? Do you understand joins, aggregations, window functions, and common data manipulation patterns? This is heavily tested at the junior level.
  • Python competence: Can you write clean, working Python for data tasks? File parsing, API calls, data transformation, and basic error handling are the expectations. You don’t need to be a software engineer, but your code should be readable and functional.
  • Pipeline thinking: Do you understand how data moves from source to destination? Can you think about extraction, transformation, loading, scheduling, monitoring, and failure recovery? This conceptual understanding is what separates data engineers from data analysts.
  • Data quality awareness: Do you think about what can go wrong with data? Missing values, duplicates, schema changes, late-arriving records — a data engineer who doesn’t think about data quality will build pipelines that silently produce bad data.
  • Learning velocity: The data engineering ecosystem evolves rapidly. Do you demonstrate curiosity about new tools and patterns? Can you pick up new technologies quickly? Junior hires are evaluated heavily on growth trajectory.

Mistakes that sink junior data engineer candidates

  1. Only knowing SQL for analysis, not for engineering. Data analyst SQL is about querying. Data engineer SQL also involves schema design, DDL statements (CREATE TABLE, ALTER TABLE), data type selection, indexing, and thinking about query performance at scale. Make sure you can discuss both.
  2. Not understanding the difference between data engineering and data science. If your answers focus on building ML models or running statistical analyses, you’ll sound like you’re interviewing for the wrong role. Data engineering is about building reliable infrastructure that enables analysis and ML.
  3. Ignoring error handling and edge cases. When you write a Python script in the interview, add error handling. What happens if the API returns a 500? What if a required field is missing? What if the file is empty? Production pipelines must handle failures gracefully.
  4. Not thinking about idempotency. If your pipeline runs twice for the same date, does it produce duplicate data? Interviewers test whether you think about this. Mention upsert strategies, deduplication, and partition overwriting.
  5. Having no opinion about tools or patterns. You don’t need to have used every tool, but you should be able to explain why you’d choose Airflow over a cron job, or when you’d use a data lake vs. a data warehouse. Having a point of view shows engagement with the field.

How your resume sets up your interview

Your resume is not just a document that gets you the interview — it’s what the interviewer will use to ask about your data engineering experience. Every pipeline, tool, or data project you mention is a potential deep-dive question.

Before the interview, review each bullet on your resume and prepare to go deeper:

  • What was the data source, and how did you extract from it?
  • What transformations did you apply, and why?
  • How did you handle data quality issues and edge cases?
  • What would you improve about the pipeline if you rebuilt it today?

A well-tailored junior data engineer resume highlights specific tools (Python, SQL, Airflow, dbt, Spark), quantified outcomes (“Built an ETL pipeline that processed 2M records daily with 99.9% uptime”), and demonstrates engineering thinking (reliability, monitoring, testing). Course projects and personal data pipelines count — present them professionally.

If your resume doesn’t set up these conversations well, our junior data engineer resume template can help you restructure it before the interview.

Day-of checklist

Before you walk in (or log on), run through this list:

  • Review the job description one more time — note the specific tools (SQL, Python, Airflow, Spark, cloud platform) and data stack mentioned
  • Prepare 3–4 STAR stories about data quality, pipeline building, and learning new tools
  • Practice writing SQL queries with window functions and CTEs without auto-complete
  • Test your audio, video, and screen sharing setup if the interview is virtual
  • Prepare 2–3 thoughtful questions about the team’s data infrastructure and pipeline practices
  • Review Python fundamentals for data tasks: file I/O, API calls, pandas, error handling
  • Have water and a notepad nearby
  • Plan to log on or arrive 5 minutes early