Databricks Data Engineer InterviewDatabricks created Apache Spark, Delta Lake, and MLflow. Their DE interviews test Spark internals, lakehouse architecture, and data governance with Unity Catalog at a depth most companies never reach. This guide covers compensation by level, the full interview process, 12 real example questions, and the specific mistakes that eliminate candidates.
Databricks
Technology · San Francisco, US
live data · June 11, 2026
DE total comp
$400K–$560K
senior level · full ladder below
Hiring now
14 open DE roles
live from career pages
Team happiness
44 / 100 · Stressed
model score from employee signals
Layoff risk (30d)
Moderate
Employee sentiment
4.0 / 5
MixedEmployees
5,001–50,000
Three stages from first recruiter call to signed offer. The entire process typically completes in 3 to 4 weeks.
Initial call covering your background and interest in Databricks. The recruiter evaluates your experience with Spark, data lakes, and lakehouse architectures. Databricks built the lakehouse category, so they expect candidates to have strong opinions about data architecture. They also probe for your understanding of why Delta Lake exists and what problems it solves.
A coding exercise focused on Spark or SQL, often both. Databricks phone screens go deeper on Spark internals than most companies. Expect questions about optimization: why a query plan looks a certain way, how to fix a skewed shuffle, or how Delta Lake handles concurrent writes. The interviewer tests whether you understand distributed processing, not just API calls.
Four to five rounds covering system design, Spark deep dive, SQL, coding, and a behavioral round. System design at Databricks involves lakehouse architectures, data governance with Unity Catalog, and MLOps pipelines. The Spark deep dive is the most differentiating round: expect questions about query plans, memory management, and performance tuning at a level most companies do not test.
Industry ranges by level.
| Level | Base | Total comp |
|---|---|---|
| JuniorL3 | $140K–$170K | $180K–$240K |
| Mid-levelL4 | $175K–$210K | $270K–$380K |
| SeniorL5 | $210K–$270K | $400K–$560K |
| StaffL6 | $255K–$320K | $550K–$800K |
| PrincipalL7 | $300K–$380K | $800K–$1.2M |
What Databricks expects at each level. Interview difficulty scales with level, especially the Spark internals round.
Implements features within a well-defined scope. Writes production Spark jobs and Delta pipelines with guidance. Expected to ramp quickly on Databricks internal tooling and contribute to team sprints within the first month.
Designs and owns components end to end. Leads the technical design of a pipeline or service, makes tradeoff decisions independently, and mentors E3 engineers. Owns on-call rotations and incident response for their domain.
Leads cross-team projects that span multiple quarters. Defines technical strategy for their area, drives alignment across teams, and is the go-to expert for at least one critical system. Influences product roadmap through technical insight.
Shapes product direction and long-term technical vision. Operates at the intersection of engineering and product strategy. Defines new capabilities that become differentiators for the Databricks platform. Recognized as a company-wide technical authority.
What their data engineers work with day to day. Worth brushing up on the heavy hitters before the loop.
Tools and platforms
Understanding which team you are interviewing for helps you tailor your preparation. Ask your recruiter which team the role is on.
Spark engine internals, Photon vectorized execution engine, cluster management, and autoscaling. The team that keeps Spark fast and reliable at massive scale.
Delta Lake transaction protocol, storage optimization (compaction, Z-ordering, liquid clustering), and cross-cloud storage abstraction. Owns the foundation of the lakehouse.
Databricks SQL product, Photon query engine, cost-based optimizer, and serverless SQL warehouses. Focused on sub-second query latency on petabyte-scale data.
Centralized metadata management, fine-grained access control, data lineage, audit logging, and cross-workspace governance. Core to Databricks enterprise sales.
MLflow open-source project, Feature Store, Model Serving, vector search, and Mosaic AI integrations. Bridges the gap between data engineering and machine learning.
Customer-facing product features: Delta Live Tables, Databricks Workflows, Auto Loader, structured streaming, and the notebook experience for pipeline development.
Reported questions from this company's loops, tagged by domain, round, and level.
Track the last 3 jobs each user launched and their status, sorted by launch date
From DataLemur Databricks SQL questions page. Schema: Users(user_id, account_created_date, last_login), Jobs(job_id, user_id, launched_date, description, status). Expected approach: use ROW_NUMBER() window function partitioned by user_id ordered by launched_date DESC, then filter for row_number <= 3 in outer query. Output sorted by launched_date descending.
Take-home: given a dataset of nested JSON records, read it in PySpark, flatten all nested structures into a tabular format, then answer 8 ETL analysis questions on the result
Design a system to synchronize two continuously updated, schema-different hotel inventory databases into a unified view.
System design question from Databricks Data Engineer onsite loop. Candidate must address: (1) Change Data Capture for detecting updates in both source databases, (2) schema mapping and reconciliation between different schemas representing the same logical entities, (3) conflict resolution strategy for concurrent updates to the same logical entity from both sources, (4) eventual consistency guarantees across the unified view, (5) recovery and re-sync from failures. Expected discussion of Delta Lake for unified storage layer, Apache Kafka for streaming change events, and schema registry for…
Complete a SQL assessment covering multiple joins, window functions, and advanced querying techniques.
Described as 'quite challenging' — required multiple joins, window functions, and advanced SQL. Part of a structured competency assessment stage at Databricks (London hiring pipeline, 2025).
Write pseudocode for binary search; also covers DBMS basics, Spark fundamentals, and OOP concepts.
2-phase interview for a college-hire DE role. Phase 1 included pseudocode for binary search and a string size finder. Phase 2 required DBMS, Spark, and OOP knowledge.
Why interviewing at Databricks requires a different preparation strategy than other data platform companies.
Databricks created Apache Spark, Delta Lake, and MLflow. Interviewers are often the original authors of these systems. Surface-level knowledge is immediately obvious. The expectation is that you understand not just how to use these tools, but why they were designed the way they were.
Databricks is one of the most valuable private tech companies, with a valuation exceeding $60 billion as of early 2026. RSU grants vest over four years and represent a meaningful portion of total compensation. The equity upside potential at E5 and above makes Databricks comp competitive with public FAANG offers.
Most companies ask you to write a SQL query or design a pipeline. Databricks asks you to explain what happens inside the engine when that query runs. Expect questions about shuffle internals, memory pressure, task scheduling, and fault recovery that you would not encounter at a typical data platform company.
Spark, Delta Lake, MLflow, and Unity Catalog all have open-source components. Databricks engineers contribute to open-source projects and engage with the community. Candidates who have contributed to or deeply studied these open-source projects have a meaningful advantage.
Patterns that consistently lead to rejections in Databricks DE interviews.
Candidates who only know the DataFrame API without understanding what happens underneath will struggle. Databricks interviewers ask about query plans, shuffle behavior, memory management, and task scheduling. You need to explain why something is slow, not just how to make it faster.
Delta Lake is a storage layer built on top of Parquet, not a file format. Candidates who say 'Delta is just Parquet with a transaction log' miss the point. Understand ACID guarantees, schema enforcement, schema evolution, time travel, and how the transaction protocol handles concurrent writes.
Databricks is investing heavily in Unity Catalog. System design answers that skip access control, lineage, and audit are incomplete. Always include a governance layer in your architecture and explain how data access policies propagate across the lakehouse.
Saying 'use Z-ordering' without explaining when it helps and when it does not is a red flag. Databricks interviewers probe for nuance: Z-ordering helps range queries but adds write overhead. Liquid clustering is better for tables with evolving access patterns. Know the tradeoffs.
Databricks is a high-growth company navigating IPO readiness. They look for engineers who can drive alignment across teams, handle ambiguity, and communicate technical decisions to non-technical stakeholders. Generic STAR answers without Databricks-relevant context fall flat.
Four areas where targeted preparation makes the biggest difference.
Databricks created Spark. Interview questions go deeper than 'use broadcast join.' Know the Catalyst optimizer, Tungsten memory management, adaptive query execution, and how to read Spark UI DAGs. This is the single biggest differentiator.
Understand Delta Lake deeply: the transaction log (_delta_log), ACID semantics, time travel, Z-ordering, OPTIMIZE/VACUUM, and change data feed. Know how Delta differs from Iceberg and Hudi and why Databricks chose this approach.
Unity Catalog is Databricks' answer to data governance: centralized access control, lineage tracking, and audit logging across all data assets. Understand its role in the lakehouse architecture and how it enables data mesh patterns.
Databricks believes the lakehouse replaces both data warehouses and data lakes. Understand the thesis: open formats, unified batch and streaming, SQL and ML on the same data, and governance as a first-class feature. Be ready to discuss tradeoffs honestly.
Problems on the platform tagged and predicted for Databricks loops, from live listings and interview reports.
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 //.
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.
What candidates reported about the loop, in their own words.
1 candidate interview report
real submissions · parsed from Glassdoor
The interview started with questions based on my resume, followed by detailed discussion on my projects, technical concepts, challenges I faced during development, some theoretical questions, and a few basic aptitude and problem-solving questions
Active recall beats re-reading by 50%
Cognitive-science meta-reviews (Dunlosky et al., 2013) rank practice testing as a top-tier study technique, while re-reading and highlighting rank near the bottom
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
Five problem shapes cover 80% of data engineer loops
Parsing and reshaping, sessionization, dedup with tie-breaks, streaming aggregation, top-N-per-group. Writing them by hand turns the unfamiliar into pattern recognition