Databricks Data Engineer Interview
At Databricks, the 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
Tech stack
What Databricks data engineers actually use
Tools and languages mentioned most often in Databricks's currently-active data engineer data engineer postings. Each chip links to an interview prep page for that tool.
Round focus
Domain concentration by round
What each Databricks round typically tests, weighted across 5 live data engineer postings. The bars show the relative emphasis of each domain.
Online Assessment
Phone Screen
Onsite Loop
Walk into Databricks knowing the Python pattern they'll test.
Practice problems
Databricks data engineer practice set
Practice sets surfaced for Databricks data engineer candidates by the same model that reads their job postings. Each card opens a working coding environment.
Active Duo
The growth team is building a cross-engagement segment of users who both make purchases and log browsing sessions on the platform. Return a deduplicated list of usernames for users with activity in both areas.
Quantile Calculator
Given a list of numbers and percentile (0-100), return the value at that percentile using linear interpolation. The index is percentile / 100 * (n - 1); if fractional, linearly interpolate between the floor and ceiling indices of the sorted values.
Users Who Churned in February
Find all users who had sessions in January {{YEAR}} but none in February {{YEAR}}.
Data Quality Report
Given a list of record dicts, return a dict per column name with 'null_count' and 'non_null_count'. Consider a value null when it is Python None.
Top 2 sellers by revenue in each marketplace
Classic DE round opener. Window function + partition. Edit to tweak the threshold.
The Narrow Lens
A narrow timeframe. Everything inside matters.
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 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 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
Other guides you'll want
FAQ
Common questions
- How much does a Databricks Data Engineer make?
- Total compensation for Databricks Data Engineer ranges $175K–$210K base • $270K–$380K total. Ranges shift by team and negotiation.
- 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.