Top 50 Data Engineer Interview Questions & Answers 2026 (SQL, PySpark & Architecture)
Key Takeaways — Data Engineering Interviews 2026
- •Core Evaluation Pillars: 1. Advanced SQL (Windowing, CTEs, self-joins), 2. Distributed Computing (Apache Spark / PySpark internals), 3. Data Lakehouse Architecture (Delta Lake, Iceberg), and 4. Pipeline Orchestration (Airflow, dbt).
- •PySpark Shuffling & Skew: Top interviewers probe memory spillage to disk, broadcast hash joins for small dimension tables, and salting keys to resolve data skew.
- •Data Modeling Patterns: Dimensional modeling (Kimball Star Schema vs Inmon Snowflake), Slowly Changing Dimensions (SCD Type 1, 2 & 3), and Change Data Capture (CDC).
- •Idempotency in Pipelines: Writing ETL/ELT pipelines where re-running for a given date partition does not produce duplicate records or corrupt downstream analytics.
1. Apache Spark & PySpark Architecture Internals
In interviews at Netflix, Uber, Walmart, Amazon, and top fintechs, PySpark questions test your understanding of distributed execution under the hood:
Q: What is the difference between Narrow and Wide Transformations in Spark?
map(), filter(), withColumn(). Fast and scalable.groupBy(), join(), distinct(), repartition().Q: How do you handle Data Skew in PySpark Joins?
Data skew occurs when an unbalanced partition key causes 95% of data to bottleneck on a single Spark executor, causing OutOfMemory (OOM) errors or endless task stages.
broadcast(small_df) to send copies to all executors, eliminating the shuffle completely!# Broadcast Join in PySpark
large_transactions_df = spark.read.parquet("s3://data/transactions")
small_stores_df = spark.read.parquet("s3://data/stores")
joined_df = large_transactions_df.join(
F.broadcast(small_stores_df),
on="store_id",
how="inner"
)
2. Advanced SQL for Data Engineers (Window Functions & CTEs)
SQL accounts for 40% of every data engineering technical screening. You must know how to use analytical window functions effortlessly:
Problem: Deduplicate Customer Records Keeping the Latest Timestamp
SELECT
customer_id,
email,
updated_at,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY updated_at DESC
) as rn
FROM customers
)
SELECT customer_id, email, updated_at
FROM RankedCustomers
WHERE rn = 1;
ROW_NUMBER() assigns unique integers 1, 2, 3... RANK() assigns identical ranks with gaps (1, 2, 2, 4), and DENSE_RANK() assigns identical ranks without gaps (1, 2, 2, 3).
Problem: Month-over-Month Revenue Growth with LAG()
month,
monthly_revenue,
LAG(monthly_revenue, 1) OVER (ORDER BY month) AS prev_month_revenue,
ROUND(
((monthly_revenue - LAG(monthly_revenue, 1) OVER (ORDER BY month)) /
LAG(monthly_revenue, 1) OVER (ORDER BY month)) * 100, 2
) AS mom_growth_pct
FROM monthly_sales;
3. Data Modeling: Star Schema, Snowflake & SCDs
Interviewers want to see if you understand warehouse design for analytical query efficiency:
A centralized Fact Table (containing numeric metrics, e.g. amount, quantity) joined directly to denormalized Dimension Tables (customer, product, store). Highly optimized for fast OLAP slice-and-dice queries with minimal joins.
Dimension tables are normalized into sub-dimensions (e.g., product → product_subcategory → product_category). Reduces data redundancy but requires complex multi-table joins during BI reporting queries.
Slowly Changing Dimensions (SCDs):
- SCD Type 1: Overwrites old data with new data (No historical tracking).
- SCD Type 2: Creates a new record with effective date range (
start_date,end_date,is_currentflag). Retains complete audit history! - SCD Type 3: Adds a new column to store previous value (e.g.,
current_city,previous_city). Tracks partial history.
4. Modern Data Lakehouses: Delta Lake, Iceberg & Parquet
Why has the industry migrated from traditional data warehouses to open table formats like Delta Lake and Apache Iceberg on top of Apache Parquet?
- ACID Transactions on Object Storage: Atomic commits prevent concurrent readers from seeing partial writes or corrupted S3 partitions.
- Time Travel: Query previous snapshots of data for machine learning reproducibility or rollback after accidental data drops.
- Schema Evolution & Enforcement: Prevent incoming bad data schemas from corrupting downstream gold tables.
- Partition Pruning & Z-Ordering: Colocate related data along multidimensional curves to skip scanning 90% of file footers during queries.
5. ETL Pipeline System Design & Airflow Orchestration
When asked to “Design an end-to-end data pipeline for a food delivery platform processing 50M events/day”:
6. Frequently Asked Data Engineering Questions
What is the difference between ETL and ELT?
In ETL (Extract, Transform, Load), data is transformed on an external compute server before loading into the warehouse. In ELT (Extract, Load, Transform), raw data is loaded directly into scalable cloud warehouses/lakehouses (Snowflake, BigQuery, Databricks) and transformed in-place using SQL/dbt.
Why is Parquet preferred over CSV/JSON for big data?
Parquet is columnar, allowing query engines to read only the specific columns queried. It provides massive compression ratios (Snappy/Gzip) and stores column min/max statistics in file footers for instantaneous partition pruning.
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