To land a role at Amazon, mastering Amazon data engineer Python interview questions is key. This guide focuses on real tasks, production-minded solutions, and short talk tracks you can use in interviews.

Python’s usage rose sharply in 2025; it reached 57.9% in Stack Overflow’s 2025 Developer Survey¹, reflecting strong demand for Python in data and AI work. McKinsey & Company reports that Amazon continues to advertise active data engineer roles across regions, underscoring steady hiring demand.

In this article, we’ll give you interview-style Python and PySpark problems with copy-paste solutions, short edge-case checks, concise talk tracks, a 300-word pipeline design, and a focused 4-week practice plan.

Key Takeaways

Amazon Data Engineer Python Interview Process Explained

Amazon’s data engineering interviews are known to be rigorous and structured. You can expect multiple stages, each evaluating both technical and soft skills. Common stages include:

1. Online Assessment (OA)

A timed coding exam (often on platforms like HackerRank) focusing on SQL, data manipulation, and basic Python tasks. You might be given scenario-based problems to test data querying and processing under time pressure.

2. Technical Phone Screen

A 45–60 minute live call where you’ll write SQL queries, discuss schema design, and possibly code in Python. Interviewers typically ask about data modeling, e.g., dimensional vs. relational schemas and fundamentals of ETL design, such as how to build a pipeline using AWS Glue, S3, and Redshift. Clear communication of your reasoning is key in this round.

3. Onsite/Virtual Loop

A series of 4–5 back-to-back interviews (often 45–60 minutes each). These rounds usually include:

• SQL/ETL round: Complex queries involving joins, window functions (e.g. ROW_NUMBER()/RANK()), aggregations, and debugging data pipelines.
• Data modeling round: Designing or optimizing schemas (star vs. snowflake, partitioning strategy) for large-scale data.
• Coding/Data structures round: Writing code or pseudocode in Python to solve data-processing problems. You may be asked to handle large files, optimize Python scripts, or implement algorithms efficiently.
• Behavioral round: Questions based on Amazon’s Leadership Principles using the STAR method. Expect questions like: “Tell me about a time you improved a data pipeline,” or “Describe a situation where you took full ownership of a project.”
• Bar-raiser interview: A final round with a senior Amazon engineer focusing on scale, judgment, and cultural fit. This often includes open-ended or cross-functional questions to assess whether you can think beyond the immediate problem and meet Amazon’s hiring standards.

Throughout each stage, Amazon evaluates not only your answers but also how you arrive at them. They look for clarity in your thought process, justifying trade-offs, and alignment with leadership principles.

Additionally, run a short PySpark data engineer interview questions drill before the loop.

Key Technical Topics & Sample Questions

Amazon data engineering interviews cover a mix of programming, data, and cloud topics. Below are some core areas and example questions:

1. Python Programming & Libraries

Python is central to data engineering. Expect questions on core Python concepts, data structures, and libraries (Pandas, NumPy, etc.) that are used in data pipelines. Use Python data engineer interview questions and answers as the backbone of practice. For example:

Each of these questions tests both your Python syntax knowledge and your practical problem-solving as a data engineer. In answers, emphasize writing clear, Pythonic code and consider scalability, e.g., using generators or batch processing to handle big data.

2. Behavioral & Leadership Questions

Amazon places huge emphasis on culture and leadership. In the loop, expect one or more rounds dedicated to behavioral questions framed around Amazon’s 16 Leadership Principles. These questions are often company-wide, not just for managers, so a data engineer candidate might hear:

• Customer obsession: Tell me about a time you put customer needs first in a project.
• Ownership: Describe a project where you had to take full responsibility from start to finish.
• Dive deep: Give an example of when you had to dig into the data to find a root cause of a problem.

Tie stories directly to Amazon data engineer Python interview questions and use Python data engineer interview questions and answers as prompts to craft crisp behavioral bullets. The goal is to show concrete examples of leadership principles and their impact on the business.

3. SQL & Data Modeling

Frame SQL examples around Amazon data engineer Python interview questions. Include the top-3 query example as a sample Amazon data engineer Python interview questions exercise. Use Python data engineer interview questions and answers to explain schema and performance trade-offs.

Add PySpark data engineer interview questions examples when discussing distributed joins and partitioning strategies.

Here are some sample questions with concise answers to use in interviews.

Q. How do you get the top 3 products by sales per region for the last 30 days?

Use a window function with partitioning and filter on row number.

SELECT product, region, total
FROM (
  SELECT product, region, SUM(sales) total,
    ROW_NUMBER() OVER (
      PARTITION BY region 
      ORDER BY SUM(sales) DESC
    ) rn
  FROM sales
  WHERE sale_date >= current_date - interval '30' day
  GROUP BY region, product
) t
WHERE rn <= 3;

Edge note: mention indexes on sale_date and grouping keys when discussing performance.

Q. How do you find duplicate customer IDs in a dimension table?

Group by the key and filter using HAVING. Example SQL

SELECT cust_id, COUNT(*) cnt
FROM dim_customer
GROUP BY cust_id
HAVING COUNT(*) > 1;

Talk track: explain why HAVING is used after aggregation and when you would add an analytic check to the pipeline.

Q. When should you pick a star schema versus a normalized model?

Choose a star schema when read performance for analytics matters and denormalization is acceptable. Pick a normalized design when write consistency and storage efficiency matter. State business assumptions like query types and update frequency.

Q. How do you avoid slow joins on huge tables?

Use partition pruning, predicate pushdown, and appropriate join keys. Consider broadcast joins for small lookup tables and composite indexes for common filters. Show cost trade-offs for memory versus shuffle.

Q. How would you design a daily engagement report from multiple source logs?

Ingest raw logs to S3, catalog with Glue, transform with Spark or Glue jobs into a fact table in Redshift, then run a partitioned report query. Mention idempotency, schema versioning, and monitoring as part of the design.

4. Big Data & AWS Services

Answer cloud design prompts with Amazon data engineer Python interview questions in mind. Describe ETL with S3, Glue, and Redshift tailored to Amazon data engineer Python interview questions.

Use PySpark data engineer interview questions to explain partitioning, salting, and broadcast joins for Amazon data engineer Python interview questions workloads. Keep paragraphs short and focused on practical checks for Amazon data engineer Python interview questions.

Below are practical Q and A pairs that cover common interview topics.

Q. How do you design an ETL pipeline using S3, Glue, and Redshift?

Ingest raw files to S3, register schemas in Glue Catalog, run Glue or Spark jobs to transform data and write Parquet to S3, then COPY into Redshift for analytics. Include IAM, partitioning, and incremental loads.

Q. How do you handle schema drift in a streaming pipeline?

Auto detect schema changes with a schema registry or Glue Catalog checks, fail fast on incompatible changes, and route unknown fields to a landing schema for manual review. Add alerts and a backfill process.

Q. How do you fix data skew in Spark joins?

Use salting for hot keys, or broadcast the small side table if it fits in memory. Repartition by join key and reduce large shuffles. Replace Python UDFs with native Spark APIs where possible.

Q. When should you use Parquet or ORC file formats?

Use Parquet or ORC for columnar storage when queries read subsets of columns. They reduce I O and improve compression. Choose Parquet for broad compatibility and ORC for some engines with heavy aggregation.

Q. How do you ensure production readiness for a PySpark job?

Make jobs idempotent, add checkpoints for streaming, tune executor memory and cores, persist hot datasets with caching, and include metrics and alerts. Use unit tests and small end-to-end runs before scaling.

Preparation Tips for Amazon Data Engineer Interviews

Success in Amazon interviews requires deliberate preparation across all the above areas. Practicing the following Amazon data engineer interview questions wil help you ace your interview and land the data engineer role at the e-commerce giant.

• Strengthen fundamentals: Brush up on SQL concepts like complex joins, window functions, and Python concepts like data structures, pandas/NumPy. Use resources like advanced Python coding challenges to cover tricky problems.
• Build projects: Create a mini data pipeline project like ingest sample data into S3, use AWS Glue or Spark for transformation, and load results into Redshift or Athena. Hands-on experience with these services (IAM permissions, data formats, partitioning) will help with both coding and design questions.
• Mock interviews: Simulate full interviews covering SQL, Python coding, and system design. Practice talking through your solution out loud, as Amazon values clear communication.
• Study leadership principles: Prepare 4–6 specific stories illustrating different principles. Each story should have a clear situation, the action you took, and the result (preferably with metrics). Review the leadership principles guide and practice answering LP questions concisely, since behavioral rounds are high-stakes.
• Time management: In the OA and coding rounds, prioritize correctness and efficiency. Practice solving data problems within time limits. During the interview, speak concisely, and if stuck, outline your intended approach rather than staying silent. Asking clarifying questions is usually better than making incorrect assumptions.
• Company research: Know recent Amazon data initiatives, like how they use Redshift, Glue, or Kinesis. Also, familiarize yourself with their products and how data insights improve customer experiences. This can help make your answers more Amazon-specific and show genuine interest.

Use Python data engineer interview questions and answers as daily checklists. Practice PySpark data engineer interview questions weekly and note performance regressions relevant to Amazon data engineer Python interview questions.

Also Read: Data Engineer Interview Questions and Answers to Practice for FAANG+ Interviews

4-Week Practice Plan for the Amazon Data Engineer Python Interview

This plan is necessary because Amazon data engineering Python interviews test depth, speed, and decision-making at scale, not just theoretical knowledge. It is designed for data engineers who already know Python and SQL but need structured, time-bound preparation to convert that knowledge into interview-ready execution.

Why Data Engineers Choose the Interview Kickstart Data Engineering Interview Masterclass?

The Data Engineering Interview Masterclass is designed for candidates preparing for Amazon and other FAANG-style data engineering interviews where depth, scale, and communication matter.
Key benefits for data engineers:

• Amazon-calibrated mock interviews that mirror real data engineer interview loops, including SQL, Python, PySpark, and system design.
• Senior engineer feedback focused on correctness, scalability, and production readiness, not just passing toy problems.
• Role-specific coverage of Amazon data engineer Python interview questions, PySpark data engineer interview questions, and real-world ETL design scenarios.
• Structured mock cadence that forces timed execution and clear verbal reasoning under interview pressure.
• Actionable gap analysis after each mock, helping candidates fix recurring mistakes in Python data engineer interview questions and answers.
• Leadership Principle alignment, ensuring technical answers map cleanly to Amazon’s evaluation criteria.

Common Mistakes and Quick Fixes

Candidates fail interviews by making repeatable, avoidable mistakes. Fixing these five areas will lift your answers for Amazon data engineer Python interview questions and for Python data engineer interview questions and answers.

Do these drills and say these lines aloud to show production thinking in interviews that ask PySpark data engineer interview questions, too.

1. Not stating assumptions

Start the Amazon data engineer Python interview questions with input, output, and constraints.

• Why it matters: Interviewers want to see you frame the problem. Clear assumptions prevent wasted work and save time.
• How to do it fast: Start with a one-sentence summary of input, output, and constraint. Use a short template and speak it out loud.
• Template to say: “I assume input is X, size is about Y, keys are Z, and we must prefer correctness over latency. I will stream if the data is large.”
• Example: “I assume a CSV with id, ts, value, size around 50GB, and no schema registry. I will stream with chunks and not load the full file.”

2. Ignoring Edge Cases

List nulls, duplicates, late arrivals, and format errors for Amazon data engineer Python interview questions. Add quick tests as part of Python data engineer interview questions and answers practice.

• Why it matters: A correct core algorithm is not enough. Edge cases break production jobs and interview signals.
• How to enumerate edge cases: List data issues, order problems, and failure modes. Common checks are nulls, duplicates, out-of-order timestamps, invalid types, and late arrivals.
• Quick checklist to run after solution:
  • Nulls present
  • Duplicate keys
  • Invalid parsing
  • Out-of-order records
  • Extreme values
• Minimal guard examples: Show one small guard in code or pseudocode. For example, parse numbers with try except, skip or log bad rows, and assert expected column set at start.

3. Premature Optimization

Deliver a correct Amazon data engineer Python interview questions prototype first.

• Why it matters: Optimizing before proving correctness wastes time and risks bugs. Interviewers prefer a correct, simple solution to measured improvements.
• A simple cadence to follow:
  • Deliver correct, readable code
  • Run a small test and a simple profiler
  • Optimize the real hotspot and explain trade-offs
• Quick optimization checklist:
  • Replace nested loops with dictionary lookups
  • Vectorize pandas operations instead of row apply
  • Use streaming or chunking for large files
  • When using memory-heavy structures, consider spilling to disk
• Mini example: If your loop is slow, replace it with a dict-based aggregation. Show the before and after complexity and a short benchmark number.

4. Poor Production Thinking

Call out idempotency, monitoring, schema evolution, and backfills when solving Amazon data engineer Python interview questions.

• Why it matters: Interviewers want to know you can run code in production, not only on a laptop. Mention idempotency, observability, and failure recovery even for simple problems.
• Concrete things to call out in an answer:
  • Idempotent writes using staging and atomic rename
  • Checkpoints and offsets for streaming jobs
  • Metrics to emit like rows processed, null rate, and processing lag
  • Retry strategy and backoff
  • Who owns the data and the runbook link?
• Small patterns to reference: Write to a staging directory, then move to final; use unique idempotency keys for upserts; emit a single metric per job run for alerting
• One line talk track to use: “I will write to a staging prefix and rename on success to make the job idempotent, and emit row count and late event metrics to CloudWatch.

5. Relying on Python UDFs in Spark

Prefer native Spark expressions for Amazon data engineer Python interview questions tasks.

• Why it matters: Python UDFs often kill performance by forcing serialization and bypassing Spark optimizations. Interviewers see this as a scale blind spot.
• Decision flow to follow when answering:
  • Can this be expressed with Spark SQL or built-in functions? If yes, use that
  • If no, and logic is row-wise, consider a Pandas UDF
  • If still problematic, precompute outside Spark or implement in a JVM UDF
• Alternatives and examples: Use when and col expressions for simple transforms; use broadcast joins instead of map-side Python lookups; for vectorized work, use Pandas UDF with Arrow to reduce overhead.
• Short example to say: Instead of a Python apply, I use Spark native when expressions so Catalyst can optimize the plan. If the logic is complex, I prototype in pandas and port to a Pandas UDF.

Conclusion

Mastering Amazon data engineer Python interview questions is a practical exercise in correctness, scale, and communication. Start by solving concrete problems and validating results on realistic data.

Keep practicing Python data engineer interview questions and answers, and PySpark data engineer interview questions. Use timed mocks to sharpen delivery and practice concise talk tracks that map to leadership principles. Along with this, practice the 4-week plan to track the Amazon data engineer Python interview questions progress and measure improvement.

Focus on iterative improvement, measure gains after each mock, and keep your study tightly scoped. With deliberate practice on Python, SQL, PySpark, and system design, you will improve both solution quality and interview presence.

FAQs: Amazon Data Engineer Python Interview Questions

Q1. What is the fastest way to improve SQL window function speed?

Practice partitioned ranking for Amazon data engineer Python interview questions and study the explain plans. Review Python data engineer interview questions and answers that cover window tuning. Run short drills and measure latency for Amazon data engineer Python interview questions examples.

Q2. How do I show scale thinking if I lack production experience?

Build a mini ETL and label it as a case for Amazon data engineer Python interview questions. Add notes answering Python data engineer interview questions and answers about scale and cost trade-offs. Include PySpark data engineer interview questions, design notes, and explain partitioning choices for Amazon data engineer Python interview questions.

Q3. Should I use Pandas or PySpark in an interview solution?

Use pandas for quick prototypes and then map to PySpark data engineer interview questions for production. Mention Amazon data engineer Python interview questions, trade-offs, and when to switch from pandas to PySpark. State explicit thresholds and memory trade-offs as part of Python data engineer interview questions and answers.

4. What do Amazon interviewers expect in a systems design answer?

They expect idempotency, monitoring, and schema evolution to be explained for the Amazon data engineer Python interview questions. Use Python data engineer interview questions and answers to show concrete checks and one metric to monitor. Add a brief partitioning rationale drawn from PySpark data engineer interview questions, thinking.

Q5. How many mock interviews should I do before applying?

Do at least three full mocks focused on Amazon data engineer Python interview questions and iterate on Python data engineer interview questions and answers. Quality feedback beats raw volume for Amazon data engineer Python interview questions improvement.

References

1. The Rising Demand for Python in Data Engineering