How much does a Databricks Junior Data Engineer make?

Across 6 offer samples from 2025-2026, Databricks Junior Data Engineer total compensation lands at $157K (P25), $183K (median), and $201K (P75), median base $119K and median annual equity $50K. Typical experience range: 2-4 years..

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.

Interview Guide · 2026

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

Compensation

Databricks Junior Data Engineer total comp

Across 6 samples

Offer-report aggregate, 2025-2026. Level mapped: L3. Typical experience: 2-4 years (median 2).

25th percentile

$157K

Median total comp

$183K

75th percentile

$201K

Median base salary

$119K

Median annual equity

$50K

Tech stack

What Databricks junior data engineers actually use

Across 1 open roles

Frequency of each tool across Databricks's open DE postings. The ones with interview prep pages are live links.

AWS1Azure1Databricks1 Delta Lake1Docker1GCP1Java1Kubernetes1MLflow1Python1 Scala1 Spark1 SQL1

Round focus

Domain concentration by round

Across 1 job descriptions

Databricks's round-by-round focus, inferred from 1 active junior data engineer job descriptions. Use this to calibrate which domains to drill for each round.

Online Assessment

Python85%

SQL44%

Architecture23%

Modeling3%

Phone Screen

Python65%

SQL64%

Architecture39%

Modeling8%

Onsite Loop

Architecture68%

Modeling31%

SQL28%

Python24%

Practice problems

Databricks junior data engineer practice set

4 problems

Problems the Databricks junior data engineer loop tends to ask, surfaced from signals in current job descriptions. Click any to start practicing.

SQLeasy~5 min

All Infra Regions

Return DISTINCT region values from infra_nodes as a single column.

Open in practice environment→

Pythonmedium~10 min

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.

Open in practice environment→

Architecturemedium~25 min

The Queue That Wouldn't Stop Growing

Your streaming video event pipeline shows consumer lag spiking from near-zero to over 500,000 messages within two hours. You need to diagnose whether the cause is a producer burst or a consumer slowdown, then design a monitoring and auto-remediation system that can detect, alert on, and automatically recover from future lag events.

Open in practice environment→

SQLeasy~5 min

Auth Service Health Checks

Return every column of every svc_health row where svc_name equals 'auth-svc' exactly.

Open in practice environment→

Try itTop 2 sellers by revenue in each marketplace

Classic DE round opener. Window function + partition. Edit to tweak the threshold.

top_sellers.sql

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WITH seller_totals AS (
  SELECT marketplace, seller_id, SUM(amount) AS revenue
  FROM seller_orders
  GROUP BY marketplace, seller_id
),
ranked AS (
  SELECT marketplace, seller_id, revenue,
    DENSE_RANK() OVER (
      PARTITION BY marketplace ORDER BY revenue DESC
    ) AS rk
  FROM seller_totals
)
SELECT marketplace, seller_id, revenue
FROM ranked
WHERE rk <= 2
ORDER BY marketplace, revenue DESC;
WITH seller_totals AS (
  SELECT marketplace, seller_id, SUM(amount) AS revenue
  FROM seller_orders
  GROUP BY marketplace, seller_id
),
ranked AS (
  SELECT marketplace, seller_id, revenue,
    DENSE_RANK() OVER (
      PARTITION BY marketplace ORDER BY revenue DESC
    ) AS rk
  FROM seller_totals
)
SELECT marketplace, seller_id, revenue
FROM ranked
WHERE rk <= 2
ORDER BY marketplace, revenue DESC;

Click Run to execute. Edit the code above to experiment.

The loop

How the interview actually runs

01Recruiter screen

30 min

Databricks 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 min

Spark-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 min

Advanced 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 min

Design 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.

“Tell me about a time you significantly improved a downstream user's workflow.”

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.

“Describe how you've influenced technical decisions beyond your immediate project.”

Go fast with high quality

Databricks ships frequently to enterprise customers where bugs are expensive. Speed + quality is a real cultural tension.

“Tell me about a time you had to deliver under a tight deadline without cutting quality.”

Be open and direct

Databricks leadership emphasizes direct communication. Avoiding hard conversations is a negative signal.

“Describe a hard conversation you had with a teammate.”

Prep timeline

Week-by-week preparation plan

8 weeks out

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)

6 weeks out

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

4 weeks out

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

2 weeks out

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

Week of

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

Related pages on Databricks's loop

Databricks Data Engineer→Databricks Senior Data Engineer→Databricks Staff Data Engineer→Databricks Principal Data Engineer→Amazon Junior Data Engineer→Meta Junior Data Engineer→Google Junior Data Engineer→Databricks Data Engineer Salary→Data Engineer Interview Prep (hub)→

FAQ

Common questions

How much does a Databricks Junior Data Engineer make?: Across 6 offer samples from 2025-2026, Databricks Junior Data Engineer total compensation lands at $157K (P25), $183K (median), and $201K (P75), median base $119K and median annual equity $50K. Typical experience range: 2-4 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.

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Published 2026-04-20Updated 2026-04-20