Data Engineering at Databricks
Roles, Comp & Culture
An L6 staff data engineer at Databricks sits around $440K total comp from 30 verified salary datapoints. The primary Data Engineering tech consists of Delta Lake, Spark and MLflow, according to current job listings. Databricks pays data engineers above other Technology companies. Reviews put them at 4.0 on Glassdoor, a little above the middle of the pack. Employee sentiment at Databricks reads neutral and employee happiness is trending down over the past year. Layoff risk scores low for the next 30 days. 14 data engineering roles are open right now.
Databricks
Technology · San Francisco, US
live data · August 2, 2026
DE total comp
$364K median
L5 · senior level · $324K–$410K · 13 verified datapoints
Hiring now
14 open DE roles
live from career pages
Team happiness
Neutral
employee happiness
Layoff risk (30d)
Low
Employee sentiment
4.0 / 5
MixedEmployees
5,001–50,000
Databricks data engineer compensation
Each level's figure is the median of individual Databricks offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
What the Databricks signals mean
Databricks is a high-growth company navigating IPO readiness, so the trade-off candidates weigh is the intensity and technical bar of a fast-scaling org against the caliber of the platform work and the open-source engineering culture behind Spark, Delta Lake, and MLflow.
Databricks employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
Databricks data engineering tech stack
The languages, storage, and processing tools Databricks data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQLDatabricks data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Architect production-level data pipelines, including end-to-end pipeline load performance testing and optimization.
Build systems that help the agent discover and use relevant lakehouse context, including tables, notebooks, code, and cell outputs, to produce more accurate and useful results.
Develop and deepen understanding of and expertise in Data Engineering, a foundational domain in the data and AI industry
Develop tooling and infrastructure to efficiently manage and run Databricks on Databricks at scale, across multiple clouds, geographies and deployment types.
Develop tooling and infrastructure to efficiently manage and run Databricks on Databricks at scale, across multiple clouds, geographies, and deployment types.
We operate a massive, multi-cloud (AWS, Azure, GCP), multi-AI, multi-region stack that powers thousands of the world’s most demanding workloads.
Lakebase provides a managed Postgres transactional layer for application state and workloads that combine operational data, analytics, and AI.
Practice for the Databricks loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
Data engineering teams at Databricks
Which team you interview for shapes the questions. The main DE surfaces:
Runtime
Spark engine internals, Photon vectorized execution engine, cluster management, and autoscaling. The team that keeps Spark fast and reliable at massive scale.
Delta Lake and Storage
Delta Lake transaction protocol, storage optimization (compaction, Z-ordering, liquid clustering), and cross-cloud storage abstraction. Owns the foundation of the lakehouse.
SQL and Query Optimization
Databricks SQL product, Photon query engine, cost-based optimizer, and serverless SQL warehouses. Focused on sub-second query latency on petabyte-scale data.
Unity Catalog and Governance
Centralized metadata management, fine-grained access control, data lineage, audit logging, and cross-workspace governance. Core to Databricks enterprise sales.
MLflow and ML Platform
MLflow open-source project, Feature Store, Model Serving, vector search, and Mosaic AI integrations. Bridges the gap between data engineering and machine learning.
Data Engineering
Customer-facing product features: Delta Live Tables, Databricks Workflows, Auto Loader, structured streaming, and the notebook experience for pipeline development.
What makes Databricks different
The things about this company that should shape every answer you give.
They built the tools you are interviewing about
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.
Pre-IPO equity is a significant part of compensation
Databricks is one of the most valuable private tech companies. RSU grants vest over 4 years and represent a meaningful portion of total compensation. The equity upside potential at senior levels and above makes Databricks comp competitive with public FAANG offers.
The interview goes deeper on distributed systems
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.
Open source philosophy shapes the culture
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.
Preparing for the Databricks loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Databricks data engineer roles by level
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Compare Databricks with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Databricks interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
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
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition