Databricks Junior Data Engineer Interview (L3)
Hiring for Junior Data Engineer at Databricks (L3) 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
Compensation
Databricks Junior Data Engineer total comp
Offer-report aggregate, 2025-2026. Level mapped: L3. Typical experience: 2-5 years (median 2).
25th percentile
$180K
Median total comp
$220K
75th percentile
$252K
Median base salary
$140K
Median annual equity
$73K
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.
Walk into Databricks knowing the Python pattern they'll test.
Round focus
Domain concentration by round
Databricks's round-by-round focus, inferred from 8 active junior data engineer job descriptions. Use this to calibrate which domains to drill for each round.
Online Assessment
Phone Screen
Onsite Loop
Practice problems
Databricks junior data engineer practice set
Databricks-tagged practice problems for the junior data engineer level. Click any to start practicing.
The Final Sale
The catalog dashboard needs every product that has sold paired with its newest sale, since some products have hundreds of transactions but only the most recent one belongs on the page. List the products alphabetically by name, showing each product's name and category alongside that latest sale's amount and date, and when two products carry the same name let the one added to the catalog first sit higher.
What Changed Overnight
A migration ran overnight, and you have the table's columns from before it (`old_schema`) and after (`new_schema`), each a list of `{'name', 'type'}` dicts. Return which columns were added, which were removed, and which kept their name but changed type.
Fix Skewed Viewing Events Pipeline
You are the on-call data engineer at a streaming company and the nightly `viewing_engagement` Spark job just paged you: it normally finishes in 45 minutes but has been running for over two hours and is still stuck. The job joins a large `event_data` table (800M rows/day of viewing, playback, and interaction events) against a small `users` dimension (2M subscribers) on `user_id`, then produces daily engagement counts by event type and account status, and its SLA is 60 minutes. Read the Spark UI evidence to find the root cause and fix the job so it meets SLA.
The Weight of Everything Before
The lifecycle analytics team is studying how each customer's spend accumulates over their lifetime on the platform, because lifetime-value models depend on seeing the full trajectory of a buyer rather than a single snapshot. For every purchase a customer has ever made, they want to see that buyer's cumulative spend as it stood at the moment of that purchase, with each customer's history walked forward from their earliest transaction to their most recent. Produce one row per purchase showing the customer, the date of that purchase, and the total amount the customer had spent up to and including that point, laid out customer by customer and earliest to latest within each.
Top 2 sellers by revenue in each marketplace
Classic DE round opener. Window function + partition. Edit to tweak the threshold.
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 L3 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 your approach aloud. Interviewers weigh how you explain it, not only what you write
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
- What level is Junior Data Engineer at Databricks?
- On Databricks's ladder, Junior Data Engineer sits at L3. Expectations center on foundational SQL fluency and a willingness to learn production systems.
- How much does a Databricks Junior Data Engineer make?
- Across 6 offer samples from 2025-2026, Databricks Junior Data Engineer total compensation lands at $180K (P25), $220K (median), and $252K (P75), median base $140K and median annual equity $73K. Typical experience range: 2-5 years..
- 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.
50+ guides covering every round, company, and role