Top Companies / Databricks
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Databricks employs around 6,000 people and is the world's leading data and AI company, headquartered in San Francisco. Founded in 2013 by the creators of Apache Spark at UC Berkeley, Databricks built the Lakehouse architecture — a platform that unifies data warehousing and data lakes — and developed widely adopted open-source projects including Delta Lake, MLflow, and Apache Spark. The company is valued at approximately $62 billion and is one of the most valuable private technology companies in the world. LoopCV users have applied to Databricks. Here is what the data shows.
Databricks at a Glance
- Employees ~6,000
- HQ San Francisco, CA
- Open roles 200-500
- Remote policy Hybrid
- Avg. response time 2-4 weeks
- ATS Greenhouse
Is Databricks Hiring Right Now?
Pre-IPO hypergrowth phase. Data and AI platform teams expanding globally. Among the most active employers for distributed systems and ML engineers.
LoopCV applies to matching Databricks roles the moment they go live.
What's it Like to Work at Databricks?
Employee culture and work-life balance ratings for Databricks, aggregated from Glassdoor, Blind, and Levels.fyi surveys. Updated May 2026.
- Hypergrowth trajectory — meaningful equity upside still likely
- Technically ambitious culture that values deep engineering
- Strong product-market fit makes the work feel impactful
- Fast growth creates occasional org and process growing pains
- Work intensity is high for customer-facing and GTM roles
Ratings aggregated from Glassdoor, Blind, and Levels.fyi. Individual experiences vary. Data as of May 2026.
Based on 2,300+ real applications submitted to Databricks via LoopCV (Jan 2024 – Apr 2026). Covering SDE, Data Engineering, and Sales roles.
How Long Does Databricks Take to Respond to Job Applications?
Based on applications sent through LoopCV to Databricks, here is the typical response timeline:
Databricks has a response rate of around 9%. The company is actively growing across data engineering, ML engineering, solutions architecture, and enterprise sales as it expands the Lakehouse platform globally. Technical roles at Databricks are highly competitive given the company's prestige and compensation packages.
Databricks' technical interview bar is high — the company was founded by PhD researchers from Berkeley's AMPLab and retains a research-forward engineering culture. For engineering roles, expect deep algorithmic questions, distributed systems design problems, and ML systems architecture discussions. For solutions architect roles, expect both technical depth and enterprise customer scenario role-plays.
What ATS Does Databricks Use?
Databricks uses Greenhouse as its applicant tracking system. CVs are reviewed for Apache Spark, Delta Lake, and Lakehouse architecture expertise, as well as distributed data systems, ML engineering, and enterprise data platform experience. The company's core tech stack is built on Scala, Python, Java, and Spark.
Keywords That Help Pass Screening
- Apache Spark, Delta Lake, Lakehouse, MLflow, databricks platform
- Scala, Python, Java, distributed data processing, Kafka, dbt
- Data engineering, ETL/ELT pipelines, data warehouse, data lake, data platform
- Machine learning engineering, MLOps, model training at scale, LLM fine-tuning
- Solutions architecture, enterprise data strategy, cloud (AWS, Azure, GCP)
Databricks invented the Lakehouse architecture and created Apache Spark, Delta Lake, and MLflow — all of which are now industry-standard tools. Candidates who have real hands-on experience with these technologies in production environments are far more compelling than those with only theoretical knowledge. If you have built production Spark pipelines or deployed MLflow tracking, describe the scale and business impact specifically.
How to Get a Job at Databricks
Databricks is one of the most technically prestigious data and AI companies in the world. Here is how to position yourself successfully.
Demonstrate production-scale Lakehouse or Spark experience
Databricks is the company that built the Lakehouse architecture and Apache Spark. Candidates who have worked with these technologies at production scale — terabyte or petabyte-scale data pipelines, Delta Lake table management, real-time streaming with Spark Structured Streaming — have a direct and significant advantage. Be specific about the data volumes, latency requirements, and business outcomes from your experience.
Show ML engineering and MLOps depth
MLflow, Databricks' open-source ML lifecycle management platform, has become an industry standard. Databricks hires ML engineers and MLOps specialists who can build, track, and serve models at enterprise scale. Candidates with experience in feature stores, model registries, model serving infrastructure, or LLM fine-tuning and deployment are particularly competitive as Databricks invests in AI platforms.
Target solutions architect roles if you have customer-facing experience
Databricks' revenue model depends heavily on solutions architects who work directly with enterprise customers to design and deploy Lakehouse architectures. These roles combine deep technical knowledge with customer communication skills. Candidates who have previously worked as data architects, principal data engineers, or implementation consultants with enterprise data platform experience are well-positioned for this track.
Prepare for a high technical bar in engineering interviews
Databricks was founded by research scientists and the technical interview bar reflects that. Engineering interviews include algorithmic coding, distributed systems design, and often ML systems architecture for relevant roles. Leetcode preparation is necessary but insufficient — practice distributed system design problems specifically in the context of data processing: designing a distributed sort, a streaming join, or a fault-tolerant pipeline.
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Databricks' Culture and Values
Databricks has a research-forward, technically rigorous culture shaped by its academic origins and its mission to democratise data and AI.
Databricks' open-source strategy is genuine and central to the company's competitive moat. Apache Spark has over a thousand contributors globally; Delta Lake and MLflow have massive community adoption. Candidates who have contributed to any of these open-source projects, even in small ways, should mention it prominently — it is a strong cultural signal at a company that measures its impact partly by GitHub stars and PyPI downloads.
Databricks Interview Questions (2026)
Real questions asked in Databricks interviews and how to answer them, based on candidate reports and hiring data.
All three are open table formats adding ACID transactions, schema evolution, and time travel to data lakes. Delta Lake (Databricks-born, Linux Foundation since 2019) has the richest Spark integration and largest enterprise adoption. Iceberg (Netflix-born) has better multi-engine support (Snowflake, Athena, Spark equally). Hudi (Uber-born) was optimised for streaming upserts first. The choice often comes down to engine ecosystem and upsert/streaming requirements.
Cover: storage layer (cloud object storage + Delta Lake), compute layer (Databricks clusters, auto-scaling, cluster policies), governance (Unity Catalog for fine-grained access control, lineage), serving layer (SQL warehouse for BI tools, MLflow for model serving), and how you'd handle mixed workloads (ETL, interactive SQL, ML training) without one workload starving another.
Be specific about the bottleneck (shuffle-heavy Spark job, skewed data partition, unnecessary full-table scans), the profiling approach (Spark UI, query plan analysis), the fix (broadcast join, partition pruning, Z-order clustering), and the measured improvement. Databricks interviews go deep on Spark internals.
Photon is a vectorised query engine written in C++ that replaces the JVM-based Spark execution engine. Cover: columnar processing with SIMD instructions, better CPU cache utilisation, reduced GC overhead, and which workloads benefit most (SQL-heavy, large aggregations, joins). It's most impactful for SQL warehouse workloads, less so for ML training.
Cover: the two serving paths (online store for low-latency inference, offline store for training), feature computation pipelines, point-in-time correctness for training data (avoiding feature leakage), feature reuse across teams, and versioning. Discuss how Databricks Feature Store integrates with MLflow for end-to-end experiment tracking.
Databricks Salaries by Level (2026)
Estimated total compensation for Databricks roles in the US, based on publicly available data from Levels.fyi, Glassdoor, and H-1B disclosure records. Figures represent annual total compensation (base + bonus + equity annualised).
| Role | Level | Total Comp | Base | Equity |
|---|---|---|---|---|
| Software Engineer | IC3 | $185k–$290k | $150k–$180k | $30k–$95k/yr |
| Senior Software Engineer | IC4 | $260k–$420k | $185k–$220k | $65k–$175k/yr |
| Staff Engineer | IC5 | $365k–$580k | $220k–$260k | $125k–$275k/yr |
Salary estimates are approximate and based on publicly reported data as of 2026. Individual offers vary by location, experience, and negotiation. Always verify with current sources.
Does Databricks Sponsor H-1B Visas?
Databricks sponsors H-1B and PERM. As a late-stage startup valued at $43B+, equity packages are significant. Heavy university research collaboration (Berkeley, MIT) creates strong PhD and international hire pipelines. Amsterdam and London offices provide EU pathways.
Databricks Job Applications - Frequently Asked Questions
Common questions from job seekers applying to Databricks. .
How long does Databricks take to respond?
Databricks typically responds within 2-4 weeks for qualified candidates. The full process from application to offer takes 5-8 weeks. Solutions architect and ML engineering roles are currently among the most active hiring areas.
What ATS does Databricks use?
Databricks uses Greenhouse. Tailor your CV with data platform keywords: Apache Spark, Delta Lake, Lakehouse, MLflow, data engineering, distributed systems, or cloud data architecture (AWS, Azure, GCP) depending on your target role.
Is Databricks a public company?
No. Databricks is private as of 2025, with a valuation of approximately $62 billion following its most recent funding round. The company has been preparing for an eventual IPO but has not set a public timeline. Employee equity is in private stock, which is illiquid until a liquidity event.
What is the Databricks Lakehouse architecture?
The Lakehouse combines the low-cost storage and flexibility of a data lake with the performance and reliability features (ACID transactions, schema enforcement) traditionally only available in data warehouses. Delta Lake provides these capabilities on top of cloud object storage. Databricks invented this architecture and it is now widely adopted across the industry.
Does Databricks hire outside of engineering?
Yes. Databricks has significant hiring across enterprise sales (account executives, sales engineers), solutions architecture, customer success, professional services, and corporate functions. Enterprise sales roles are particularly active as Databricks expands globally. Sales candidates with data platform or cloud infrastructure backgrounds are competitive.
How can LoopCV help me apply to Databricks?
LoopCV monitors Databricks' Greenhouse job board and automatically applies to matching roles in data engineering, ML engineering, solutions architecture, and enterprise sales the moment new positions are posted. Given Databricks' prestige and the competition for its roles, applying early matters significantly.
Auto-Apply to Databricks with LoopCV
Databricks is one of the most technically prestigious and well-compensated data companies in the world. LoopCV monitors Greenhouse and applies automatically the moment a matching role is posted.