Databricks Junior Data Engineer Interview
Hiring for Junior Data Engineer at Databricks runs Spark-and-Delta-deep technical expectations, customer-facing engineering mindset. The hiring bar is foundational SQL fluency and a willingness to learn production systems; the median candidate brings 0-2 years of DE experience.
Compensation
$140K–$170K base • $180K–$240K total
Loop duration
3 hours onsite
Rounds
4 rounds
Location
San Francisco, Seattle, NYC, Mountain View, remote for select roles
Tech stack
What Databricks junior data engineers actually use
Frequency of each tool across Databricks's open DE postings. The ones with interview prep pages are live links.
Round focus
Domain concentration by round
Databricks's round-by-round focus, inferred from 14 active junior data engineer job descriptions. Use this to calibrate which domains to drill for each round.
Online Assessment
Phone Screen
Onsite Loop
Walk into Databricks knowing the Python pattern they'll test.
Practice problems
Databricks junior data engineer practice set
Problems the Databricks junior data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.
Full Customer Order List
Return first_name, last_name, and country for every customer in customers. Sort alphabetically by first_name, then last_name.
Detect Cycle in Sequence
You are given a list of integers where each value at index i is the next index to visit (or -1 to terminate). Starting from index 0, follow the chain and return True if you revisit any index, False otherwise. Out-of-range indices (including -1) count as termination, not a cycle.
High Volume Batch Jobs
Surface all batch jobs that processed more than 5000 rows, showing each job's name, priority, and rows processed, ranked from most to fewest.
The Bitwise Judge
Given an integer n (possibly negative), return True if n is even, False if odd. Solve using bitwise operations only - no %, no /, no //.
Top 2 sellers by revenue in each marketplace
Classic DE round opener. Window function + partition. Edit to tweak the threshold.
The Number Miner
JSON strings are hiding numeric secrets - dig them out.
Pulled from debriefs where Python parsing was the gate.
The loop
How the interview actually runs
01Recruiter screen
30 minDatabricks hires heavily for Spark + Delta Lake expertise. The recruiter probes depth in these specific technologies.
- →Spark experience on any cloud is weighed heavily
- →Mention Delta Lake or Apache Iceberg experience
- →Customer-facing DE roles (CSE, Field Engineering) have different tracks
02Technical phone screen
60 minSpark-focused coding. Expect optimization questions, partition-skew handling, broadcast vs shuffle decisions, Delta Lake merge semantics.
- →Know Spark physical plan reading, it comes up constantly
- →Delta Lake specifics: MERGE semantics, Z-ordering, time travel
- →Be ready to write PySpark or Scala Spark fluently
03Onsite: Spark deep-dive
60 minAdvanced Spark: solve a performance problem on a 10 TB dataset, debug a stuck job from metrics screenshots, or design a Delta Lake schema for a specific workload.
- →Physical plan, shuffle analysis, partition skew are table stakes
- →AQE (Adaptive Query Execution) is hot at Databricks, know what it does
- →Delta Lake internals: deletion vectors, liquid clustering, checkpoints
04Onsite: architecture
60 minDesign a lakehouse-oriented pipeline. Databricks expects candidates to reach for Delta Lake, Unity Catalog, and medallion architecture natively.
- →Bronze-silver-gold pattern is the default
- →Unity Catalog for governance and lineage
- →Discuss the lakehouse vs warehouse debate with nuance
Level bar
What Databricks expects at Junior Data Engineer
SQL foundations
Junior rounds weight SQL the heaviest. Expect multi-table joins, aggregations, window functions, and one harder query involving self-joins or recursive CTEs. You do not need to design systems at this level, but you do need SQL to be reflexive.
Learning orientation
Interviewers probe how you pick up new tools. A strong story about learning a new stack in a prior role (even an internship or side project) can outweigh gaps in production experience.
Basic pipeline awareness
You should know what ETL vs ELT means, what a data warehouse is, and why idempotency matters, even if you have not built a production pipeline yourself.
Databricks-specific emphasis
Databricks's loop is characterized by: Spark-and-Delta-deep technical expectations, customer-facing engineering mindset. Calibrate your preparation to that, generic FAANG prep will not close the gap on company-specific expectations.
Behavioral
How Databricks frames behavioral rounds
Customer-focused engineering
Databricks sells to data teams. DEs are expected to think about the customer experience even when not customer-facing.
Raise the bar
Databricks operates in a hiring market where 'hire above the median' is explicit. Candidates should show they've made their previous teams better.
Go fast with high quality
Databricks ships frequently to enterprise customers where bugs are expensive. Speed + quality is a real cultural tension.
Be open and direct
Databricks leadership emphasizes direct communication. Avoiding hard conversations is a negative signal.
Prep timeline
Week-by-week preparation plan
Foundations and gap analysis
- ·Do 10 medium SQL problems. Note which patterns feel slow
- ·Write out 2-3 behavioral stories per value, Databricks weights this round heavily
- ·Read Databricks's public engineering blog for recent architecture patterns
- ·Shore up data engineering foundations: SQL, Python, one warehouse (Snowflake/BigQuery/Redshift)
SQL and coding fluency
- ·Practice window functions until DENSE_RANK, ROW_NUMBER, LAG, LEAD are reflex
- ·Do 20+ Databricks-style problems in their domain
- ·Time yourself: 25 min per medium, 35 min per hard
- ·Record yourself narrating approach aloud, communication is graded
Pipeline awareness and behavioral depth
- ·Review pipeline architecture basics: idempotency, partitioning, backfill
- ·Practice explaining a pipeline you've worked on end-to-end in 5 minutes
- ·Refine behavioral stories based on mock feedback
- ·Do 10 more SQL problems at medium difficulty
Behavioral polish and mock loops
- ·Rehearse every story out loud. Cut to 2-3 minutes each
- ·Run 2 full mock loops with a mid-level DE or coach
- ·Identify your 3 weakest behavioral areas and draft additional stories
- ·Review recent Databricks news or earnings call for fresh talking points
Taper and logistics
- ·No new content. Review your notes only
- ·Sleep. Mental energy matters more than one more practice problem
- ·Confirm logistics: laptop charged, shared-doc tool tested, snack and water nearby
- ·Remember: interviewers want to find reasons to hire you, not to reject you
See also
Related pages on Databricks's loop
FAQ
Common questions
- How much does a Databricks Junior Data Engineer make?
- Total compensation for Databricks Junior Data Engineer ranges $140K–$170K base • $180K–$240K total. Ranges shift by team and negotiation.
- How is the Junior Data Engineer loop different from other levels at Databricks?
- Round structure is shared across levels; what changes is what each round tests. For Junior Data Engineer the emphasis is foundational SQL fluency and a willingness to learn production systems, with particular attention to SQL fundamentals, learning orientation, and basic pipeline awareness.
- How long should I prepare for the Databricks Junior Data Engineer interview?
- 6-8 weeks of focused prep is typical for candidates already working as a DE. Less than 4 weeks is tight; the behavioral story bank usually takes longer than candidates expect.
- Does Databricks interview data engineers differently than software engineers?
- Yes. DE loops at Databricks weight SQL heavier, include pipeline/system-design rounds tuned to data workloads, and probe for production data experience (ingestion patterns, data quality, backfill) that generalist SWE loops skip.