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Databricks
3.1
based on 24 Reviews
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Company Overview
Company Locations
Working at Databricks
Company Summary
Databricks develops advanced data processing solutions and is recognized for creating the Apache Spark framework, facilitating distributed computing.
Overall Rating
3.1/5
based on 24 reviews
14% below
industry average
Highly rated for
Salary
Critically rated for
Work-life balance, Job security, Company culture
Work Policy
Monday to Friday
100% employees reported
Flexible timing
64% employees reported
No travel
82% employees reported
Night shift
50% employees reported
View detailed work policy
Top Employees Benefits
International/On-site exposure
1 employee reported
Professional degree assistance
1 employee reported
Job/Soft skill training
1 employee reported
Child care facility
1 employee reported
View all benefits
About Databricks
Founded in2013 (12 yrs old)
India Employee Count--
Global Employee Count1k-5k
HeadquartersSan Francisco,California, United States
Office Locations
--
Websitedatabricks.com
Primary Industry
Other Industries
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View in video summary
Databricks is a company founded by the original creators of Apache Spark. Databricks grew out of the AMPLab project at University of California, Berkeley that was involved in making Apache Spark, an open-source distributed computing framework built atop Scala.
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Databricks Ratings
based on 24 reviews
Overall Rating
3.1/5
How AmbitionBox ratings work?
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3
5
2
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6
Category Ratings
3.8
Salary
3.3
Skill development
2.9
Promotions
2.9
Work satisfaction
2.8
Company culture
2.7
Job security
2.7
Work-life balance
Databricks is rated 3.1 out of 5 stars on AmbitionBox, based on 24 company reviews.This rating reflects an average employee experience, indicating moderate satisfaction with the company’s work culture, benefits, and career growth opportunities. AmbitionBox gathers authentic employee reviews and ratings, making it a trusted platform for job seekers and employees in India.
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Databricks Reviews
Top mentions in Databricks Reviews
Compare Databricks with other companies in IT Services & Consulting Industry
 |  Change Company |  Change Company |  Change Company | |
|---|---|---|---|---|
Overall Rating | 3.1/5 based on 24 reviews | 3.3/5 based on 23 reviews | 4.4/5 based on 23 reviews | 4.3/5 based on 23 reviews |
Highly Rated for | Salary | Skill development Job security | Skill development Work-life balance Company culture | Skill development Job security Company culture |
Critically Rated for | Work-life balance Job security Company culture | Company culture Work-life balance |  No critically rated category | No critically rated category |
Rating by Women Employees | 3.7 Good rated by 8 women | 4.9 Excellent rated by 5 women | 1.2 Bad rated by 2 women | 3.5 Good rated by 5 women |
Rating by Men Employees | 2.8 Poor rated by 15 men | 2.9 Poor rated by 16 men | 4.8 Excellent rated by 21 men | 4.8 Excellent rated by 16 men |
Job security | 2.7 Poor | 3.9 Good | 3.8 Good | 4.5 Good |
View more
Databricks Salaries
Databricks salaries have received with an average score of 3.8 out of 5 by 24 employees.
Data Engineer
(19 salaries)
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₹16 L/yr - ₹27 L/yr
Technical Solutions Engineer
(18 salaries)
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₹22 L/yr - ₹32 L/yr
Solution Architect
(15 salaries)
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₹44.5 L/yr - ₹80.1 L/yr
Senior Consultant
(11 salaries)
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₹30 L/yr - ₹34 L/yr
Software Developer
(11 salaries)
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₹30 L/yr - ₹43 L/yr
Cash Application Analyst
(10 salaries)
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₹12 L/yr - ₹13 L/yr
Senior Data Engineer
(9 salaries)
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₹21.7 L/yr - ₹35 L/yr
Senior Data Scientist
(9 salaries)
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₹22 L/yr - ₹33 L/yr
Senior Software Engineer
(8 salaries)
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₹26 L/yr - ₹42 L/yr
Software Engineer
(8 salaries)
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₹28.6 L/yr - ₹46 L/yr
Databricks Interview Questions
Databricks Jobs
Popular Designations Databricks Hires for
Popular Skills Databricks Hires for
Current Openings
Databricks News
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LLMOps in 2025: The Latest Trends and Best Practices for Production-Ready AI
- LLMOps landscape has transformed with organizations moving to production-scale AI deployments in 2025.
- LLMOps introduces unique challenges like natural language complexity, prompt engineering, ethical considerations.
- Current trends include specialized models, human-in-the-loop workflows, advanced observability, RAG architecture.
- Key tools in LLMOps space: LangChain, W&B, Databricks, LangSmith, Arize Phoenix, Vertex AI.
- Best practices involve prompt management, safety monitoring, cost optimization, versioning, and robust evaluation.
Dev | 17 Aug, 2025
Declarative Pipelines in Apache Spark 4.0
- Databricks, a key contributor to Apache Spark 4.0, has open-sourced a core declarative ETL framework, aiming to address operational overhead in managing data pipelines.
- The new framework extends the benefits of declarative programming from individual queries to entire data pipelines, providing a more maintainable approach for building data solutions.
- Data professionals have traditionally used Spark's APIs to imperatively define data transformations; the shift to declarative programming offers a simplified paradigm for data processing.
Dzone | 13 Aug, 2025
Declarative Pipelines in Apache Spark 4.0
- Databricks, a key contributor to Apache Spark 4.0, has open-sourced its core declarative ETL framework to simplify complex data workflows.
- The new framework extends declarative programming benefits from individual queries to entire data pipelines, enhancing robustness and maintainability.
- Data professionals are shifting from imperative to declarative models in Apache Spark 4.0, streamlining data transformation definitions.
- The focus is on offering a more efficient and intuitive way to manage large-scale data processing in evolving big data environments.
Dzone | 13 Aug, 2025
Model serving architectures
- Model serving architectures are discussed in Lecture 5 on Databricks where batch predictions are computed in advance and stored for later retrieval.
- Databricks Feature Serving is utilized for personal recommendation scenarios with low latency requirements, such as in e-commerce for product recommendations.
- Model serving can involve deploying the model behind an endpoint, and features are available through the payload, although this may not be the most realistic scenario.
- Databricks now supports various machine learning model deployment architectures, simplifying the deployment process, and the next lecture will focus on implementing a model serving endpoint for real-time predictions.
Medium | 2 Aug, 2025
Databricks Machine Learning Associate Practice Exams: 2025
- The guide provides resources for preparing with Databricks Machine Learning Associate Practice Exams in 2025, emphasizing its importance and utility across various industries.
- Key exam topics include Feature Engineering, Model Training & Tuning, MLflow and Experiment Tracking, Deployment & Monitoring, and other related areas.
- Tips for success include mastering feature creation, model building, MLflow usage, and mental preparation for the exam's challenges.
- The content has been updated for 2025, offering insights into logging metrics, practical project-based approaches, study group recommendations, and the significance of Python knowledge.
Medium | 1 Aug, 2025
☁️ Cloud Data Platforms Showdown: Snowflake vs Databricks vs BigQuery (2025 Edition)
- Snowflake, Databricks, and BigQuery are the key players in the 2025 cloud data landscape.
- Snowflake is praised for being sleek, simple, and powerful, handling tasks like data transformation effortlessly.
- Databricks is highlighted as the Machine Learning (ML) king, excelling in handling massive amounts of data for models.
- BigQuery by Google is known for its lightning-fast processing speeds, ideal for quick data analysis tasks.
- While each platform has its strengths, the choice of tool can significantly impact data analysis and reporting capabilities.
Medium | 31 Jul, 2025
AI in GTM Efficiency: The Playbooks from Databricks, Monday.com and Benchling
- Three high-growth companies share AI implementation strategies at SaaStr Annual’s AI Summit.
- Companies emphasize building on existing platforms for customization and efficiency gains with AI.
- Focus on automating manual tasks, customer intelligence, and strategic budget allocation for AI integration.
Saastr | 30 Jul, 2025
Tech Tuesday: The highest-rated customer data platforms for 2025
- Tech Tuesday dives into practical Customer Data Platforms, highlighting noteworthy tools for investment.
- Featured tools include Databricks' modular approach, Progress Software's AI-powered Sitefinity Insight, and Twilio Segment.
- Also explored are platforms like Amperity, Zoho CRM, Adobe Real-Time CDP, and more.
- Each platform offers unique features to streamline data management, enhance personalization, and drive engagement.
- From AI capabilities to real-time data activation, these tools cater to diverse business needs.
Dynamicbusiness | 29 Jul, 2025
Top AI Startups to Bet Your Career On in 2025
- Identify top AI startups for future career bets with advanced AI models.
- Companies like OpenAI, Anthropic, and Databricks offer secure career acceleration.
- List includes 16 top career bets and 64 startups worth considering for hiring.
Medium | 29 Jul, 2025
Understanding Calendar mode for Dynamic Workload Scheduler: Reserve ML GPUs and TPUs
- Organizations require ML compute resources that can adapt to fluctuating demands, necessitating cost-efficient and flexible consumption models.
- Calendar mode, a new feature in Dynamic Workload Scheduler, offers short-term ML capacity without long-term commitments, allowing reservations for up to 90 days.
- Calendar mode enhances Compute Engine future reservations by providing co-located GPU and TPU capacity suitable for various ML tasks, such as model training and inference.
- Customers have lauded Calendar mode for its flexibility and cost-effectiveness, with examples from Schrödinger, Vilya, and Databricks showcasing the benefits for diverse ML use cases.
Cloudblog | 28 Jul, 2025
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Databricks FAQs
When was Databricks founded?
Databricks was founded in 2013. The company has been operating for 12 years primarily in the IT Services & Consulting sector.
Where is the Databricks headquarters located?
Databricks is headquartered in San Francisco,California.
Does Databricks have good work-life balance?
Databricks has a work-life balance rating of 2.7 out of 5 based on 20+ employee reviews on AmbitionBox. 58% employees rated Databricks 3 or below for work-life balance. This rating reflects a negative sentiment among employees for work-life balance. We encourage you to read Databricks work-life balance reviews for more details.
Is Databricks good for career growth?
Career growth at Databricks is rated as poor, with a promotions and appraisal rating of 2.9. 58% employees rated Databricks 3 or below on promotions / appraisal. This rating reflects a negative sentiment among employees for career growth. We recommend reading Databricks reviews for more detailed insights.
What are the pros and cons of working in Databricks?
Working at Databricks comes with several advantages and disadvantages. It is highly rated for salary & benefits. However, it is poorly rated for work life balance, job security and company culture, based on 20+ employee reviews on AmbitionBox.
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