10 Interview Questions for Junior Databricks Data Engineer Roles ($80-100k)
The Fundamentals That Get You Hired
Breaking into Databricks Data Engineering? Junior interviews test whether you understand the fundamentals - not obscure edge cases.
Companies hiring at $80-100k want to see: you understand core concepts, you can learn quickly, and you have the right mental models to grow.
What’s inside:
✅ All 10 questions with detailed answers
✅ Weak vs Strong answer comparison
✅ Key concepts to mention
✅ What interviewers are really testing
🎯 Question 1: What’s the difference between a transformation and an action in Spark?
⚠️ The Weak Answer (Pass, but not impressive)
“Transformations change data, actions return results.”
Why this is weak: Technically correct but doesn’t show understanding of WHY this matters.
✅ The Strong Answer (Stands Out)
“Transformations are lazy - they build up a logical plan but don’t execute until an action triggers them. Examples: filter(), select(), groupBy(). Actions trigger actual computation and return results to the driver or write to storage. Examples: count(), collect(), show(), write(). This matters because Spark can optimize the entire chain of transformations before executing. If I call df.filter().select().filter(), Spark combines these into one optimized pass through the data.”
Key concepts to mention:
Lazy evaluation and query optimization
Transformations: filter, select, groupBy, join
Actions: count, collect, show, write
DAG (Directed Acyclic Graph) execution
Why lazy evaluation enables optimization
💡 What Interviewers Are Testing
Do you understand Spark’s execution model, or just memorize syntax? Shows you know WHY Spark works this way.
🎯 Question 2: Why would you use Delta Lake instead of regular Parquet files?
⚠️ The Weak Answer (Pass, but not impressive)
“Delta Lake is better and has more features than Parquet.”
Why this is weak: Vague - doesn’t explain specific benefits.
✅ The Strong Answer (Stands Out)
“Delta Lake adds ACID transactions on top of Parquet files. Three key benefits: 1) Reliability - if a write fails halfway, the table stays consistent (no partial writes), 2) Time travel - I can query previous versions of data or rollback mistakes, 3) MERGE support - I can do upserts which plain Parquet can’t do. Delta is still Parquet underneath, but the transaction log (_delta_log) tracks what files are valid, enabling these features.”
Key concepts to mention:
ACID transactions (Atomicity, Consistency, Isolation, Durability)
Transaction log in _delta_log folder
Time travel via VERSION AS OF
MERGE for upserts (update + insert)
Schema enforcement and evolution
💡 What Interviewers Are Testing
Do you understand why Delta Lake exists, or just that it’s “the Databricks way”? Shows foundational knowledge.
🎯 Question 3: Explain the medallion architecture (Bronze, Silver, Gold). Why is it used?
⚠️ The Weak Answer (Pass, but not impressive)
“Bronze is raw data, Silver is cleaned, Gold is aggregated.”
Why this is weak: Correct but doesn’t explain the reasoning or benefits.
✅ The Strong Answer (Stands Out)
“The medallion architecture separates data by quality level. Bronze is the raw landing zone - ingest everything, preserve the original data for reprocessing if needed. Silver applies cleaning: deduplication, data type fixes, filtering bad records. Gold is business-ready: aggregations, joins, optimized for specific use cases. The key benefit is debuggability - if something looks wrong in Gold, I can trace back through Silver to Bronze to find where things went wrong. It also lets different teams work at different layers.”
Key concepts to mention:
Bronze: raw, append-only, source of truth
Silver: cleaned, deduplicated, typed correctly
Gold: aggregated, business logic applied, query-optimized
Reprocessing capability from Bronze
Clear data lineage through layers
💡 What Interviewers Are Testing
Do you understand data pipeline design principles? Shows you can think about data quality systematically.
🎯 Question 4: What’s the difference between a cluster and a job in Databricks?
⚠️ The Weak Answer (Pass, but not impressive)
“A cluster is the compute, a job runs code.”
Why this is weak: Too brief - misses important operational details.
✅ The Strong Answer (Stands Out)
“A cluster is the compute infrastructure - a group of VMs running Spark. It has a driver node (coordinates work) and worker nodes (execute tasks). Clusters can be interactive (for development) or job clusters (spin up for one job, terminate after). A job is a scheduled execution of notebooks or scripts - it defines WHAT runs, WHEN it runs, and on WHAT cluster. Jobs can create their own job clusters (more isolated, cost-efficient) or use existing interactive clusters.”
Key concepts to mention:
Driver node vs worker nodes
Interactive clusters vs job clusters
Jobs define: what, when, which cluster
Autoscaling for variable workloads
Cost implications of cluster choice
💡 What Interviewers Are Testing
Do you understand Databricks infrastructure basics? Shows you can work in a production environment.
🎯 Question 5: How do you read a CSV file and write it as a Delta table?
⚠️ The Weak Answer (Pass, but not impressive)
“spark.read.csv(’path’).write.format(’delta’).save(’path’)”
Why this is weak: Minimal code without mentioning important options.
✅ The Strong Answer (Stands Out)
“I’d use spark.read.option(’header’, ‘true’).option(’inferSchema’, ‘true’).csv(’source_path’) to read with headers and automatic type inference. For production, I’d define the schema explicitly instead of inferring - it’s faster and catches data issues early. Then write with df.write.format(’delta’).mode(’overwrite’).saveAsTable(’my_table’). Using saveAsTable registers it in the metastore so others can find it. I’d also consider partitionBy for large tables that are commonly filtered by a column like date.”
Key concepts to mention:
Header and schema inference options
Explicit schema definition for production
Write modes: overwrite, append, merge
saveAsTable vs save (metastore registration)
partitionBy for query performance
💡 What Interviewers Are Testing
Can you do basic ETL tasks correctly? Shows practical hands-on skills.
🎯 Question 6: What happens if you call collect() on a DataFrame with 1 billion rows?
⚠️ The Weak Answer (Pass, but not impressive)
“It would be slow.”
Why this is weak: Misses the critical issue - it’s not just slow, it breaks.
✅ The Strong Answer (Stands Out)
“collect() pulls ALL data from the workers to the driver node as a Python/Scala list. With 1 billion rows, the driver would run out of memory and crash (OOM error). This is why collect() should only be used on small results - after aggregations or heavy filtering. For large datasets, use write() to save results to storage instead. If I need a sample for debugging, I’d use take(100) or limit(100).collect() to get a small subset safely.”
Key concepts to mention:
Data moves from distributed workers to single driver
Driver memory is limited (typically a few GB)
OOM (Out of Memory) error
Alternatives: write(), take(), limit()
collect() only safe after significant reduction
💡 What Interviewers Are Testing
Do you understand distributed computing basics? Shows you won’t make expensive mistakes in production.
🎯 Question 7: What’s the difference between Spark SQL and the DataFrame API?
⚠️ The Weak Answer (Pass, but not impressive)
“SQL uses SQL syntax, DataFrame uses Python/Scala methods.”
Why this is weak: Obvious - doesn’t address when to use which.
✅ The Strong Answer (Stands Out)
“Both compile to the same execution plan - performance is identical. Spark SQL (spark.sql(’SELECT...’)) is great for complex queries, especially joins and aggregations - SQL is expressive for this. DataFrame API (df.filter().groupBy()) is better when logic is dynamic or needs programmatic control - like looping through column names. In practice, I mix both: use createOrReplaceTempView() to register a DataFrame, then query it with SQL. The Catalyst optimizer handles both identically.”
Key concepts to mention:
Same execution engine underneath
Catalyst optimizer handles both
SQL better for complex analytical queries
DataFrame API better for programmatic logic
Can mix both via temp views
💡 What Interviewers Are Testing
Can you choose the right tool for the job? Shows practical judgment.
