Databricks Data Engineer Interview (L4)
At Databricks, the (L4) Data Engineer interview is characterized by Spark-and-Delta-deep technical expectations, customer-facing engineering mindset. To clear this bar you need shipped production pipelines end-to-end and can debug them when they break, built on 2-5 years of production DE work.
Compensation
$175K–$210K base • $270K–$380K total
Loop duration
3 hours onsite
Rounds
4 rounds
Location
San Francisco, Seattle, NYC, Mountain View, remote for select roles
Compensation
Databricks Data Engineer total comp
Offer-report aggregate, 2025-2026. Level mapped: L4. Typical experience: 3-7 years (median 4).
25th percentile
$193K
Median total comp
$248K
75th percentile
$360K
Median base salary
$157K
Median annual equity
$100K
Tech stack
What Databricks data engineers actually use
Tools and languages mentioned most often in Databricks's currently-active data engineer postings. Each chip links to an interview prep page for that tool.
Walk into Databricks knowing the Python pattern they'll test.
Round focus
Domain concentration by round
What each Databricks round typically tests, weighted across 8 live data engineer postings. The bars show the relative emphasis of each domain.
Online Assessment
Phone Screen
Onsite Loop
Practice problems
Databricks data engineer practice set
Problems written for Databricks, picked for data engineer candidates where the level matches. Each card opens a working coding environment.
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.
Read the Plan
The order enrichment job joins a 500M-row orders table (80 GB) against a 5,000-row stores dimension (30 MB) on store_id. The join takes 12 minutes and shuffles 80 GB. The physical plan shows SortMergeJoin with Exchange (shuffle) on both sides. The stores table is 30 MB. Why did Spark choose SortMergeJoin, and how do you fix it?
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 L4 Data Engineer
Pipeline ownership
Mid-level DEs own pipelines end-to-end. Interviewers expect stories about designing, deploying, and maintaining a data pipeline that has been in production for 6+ months.
SQL + Python or Spark fluency
SQL is the floor. Most teams also expect fluency in either Python for data manipulation (pandas, airflow DAGs) or Spark for larger-scale processing.
On-call debugging
You should have concrete stories about production incidents: what alert fired, how you diagnosed, what you fixed, and what post-mortem action you owned.
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
- ·Review your prior production work, pick 3-5 projects you can discuss in depth
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
Other guides you'll want
FAQ
Common questions
- What level is Data Engineer at Databricks?
- Databricks uses L4 to designate Data Engineers; this is an IC-track level focused on shipped production pipelines end-to-end and can debug them when they break.
- How much does a Databricks Data Engineer make?
- Databricks Data Engineer offers span $193K-$360K across 8 samples from 2025-2026, with a median of $248K, median base $157K and median annual equity $100K. Typical experience range: 3-7 years..
- How is the Data Engineer loop different from other levels at Databricks?
- Data Engineer loops run the same stages as other levels, but interviewers calibrate difficulty to shipped production pipelines end-to-end and can debug them when they break, especially around production pipeline ownership and on-call debugging.
- How long should I prepare for the Databricks Data Engineer interview?
- 6-8 weeks is the standard window for a working DE. Less than 4 weeks almost always means cutting the behavioral prep short.
- Does Databricks interview data engineers differently than software engineers?
- The tracks diverge. DE at Databricks weights SQL and pipeline-design rounds, and interviewers expect specific production data experience that SWE loops don't probe.
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