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Databricks
2.7
based on 19 Reviews
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
2.7/5
based on 19 reviews
27% below
industry average
Critically rated for
Job security, Company culture, Work-life balance
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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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 19 reviews
Overall Rating
2.7/5
How AmbitionBox ratings work?
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6
Category Ratings
3.7
Salary
3.0
Skill development
2.6
Promotions
2.5
Work satisfaction
2.4
Work-life balance
2.3
Company culture
2.2
Job security
Databricks is rated 2.7 out of 5 stars on AmbitionBox, based on 19 company reviews.This rating reflects a below average employee experience, indicating poor 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 Similar Companies
Change Company | Change Company | Change Company | ||
|---|---|---|---|---|
Overall Rating | 2.7/5 based on 19 reviews | 3.7/5 based on 92.1k reviews | 3.7/5 based on 54.4k reviews | 3.8/5 based on 58.2k reviews |
Highly Rated for | No highly rated category | Job security Work-life balance | Job security | No highly rated category |
Critically Rated for | Job security Company culture Work-life balance | Promotions Salary Work satisfaction | Promotions Salary | Promotions |
Primary Work Policy | - | Work from office 81% employees reported | Hybrid 61% employees reported | Hybrid 75% employees reported |
Rating by Women Employees | 3.8 Good rated by 6 women | 3.7 Good rated by 26.7k women | 3.8 Good rated by 15.6k women | 3.8 Good rated by 21.9k women |
Rating by Men Employees | 2.2 Poor rated by 12 men | 3.6 Good rated by 60.2k men | 3.7 Good rated by 36.3k men | 3.8 Good rated by 33.7k men |
Job security | 2.2 Poor | 4.5 Good | 3.8 Good | 3.7 Good |
View more
Databricks Salaries
Databricks salaries have received with an average score of 3.7 out of 5 by 19 employees.
Technical Solutions Engineer
(24 salaries)
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₹12 L/yr - ₹33 L/yr
Data Engineer
(16 salaries)
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₹12 L/yr - ₹26 L/yr
Solution Architect
(11 salaries)
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₹32 L/yr - ₹60 L/yr
Senior Consultant
(11 salaries)
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₹30 L/yr - ₹34 L/yr
Senior Data Engineer
(7 salaries)
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₹25 L/yr - ₹35 L/yr
Senior Technical Solutions Engineer
(7 salaries)
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₹27.3 L/yr - ₹42 L/yr
Associate Technical Solutions Engineer
(7 salaries)
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₹12 L/yr - ₹18 L/yr
Data Scientist
(6 salaries)
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₹7 L/yr - ₹24 L/yr
Senior Solution Architect
(5 salaries)
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₹48 L/yr - ₹85 L/yr
SSE
(5 salaries)
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₹37.6 L/yr - ₹40 L/yr
Databricks Jobs
Popular Designations Databricks Hires for
Popular Skills Databricks Hires for
Current Openings
Databricks News
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CoreWeave’s IPO fizzles. Is the AI data center boom about to sputter too?
- CoreWeave's IPO fell short of expectations, leading to a 5% drop in stock price on debut.
- Concerns loom over the sustainability of the AI data center building boom, with indications of slowing growth in smaller AI models.
- Delays in AI chipmaker Cerebras' IPO raise doubts due to foreign investment scrutiny.
- Despite challenges, AI models requiring significant compute power continue to gain traction, with notable advancements by Google and Microsoft.
- Major funding rounds for AI startups like OpenAI and Manus signal ongoing investor interest in the sector.
- In other news, genetic testing company 23andMe files for Chapter 11 bankruptcy, potentially putting valuable data up for sale.
- Former Intel CEO Pat Gelsinger transitions to venture capital at Playground Global, raising speculation on a return to operational roles.
- Upcoming tech events include KubeCon+CloudNativeCon in Barcelona and Robotics & AI Media Week in NYC.
- Recent advancements in AI and data include Google's Gemini 2.5 Pro and Microsoft's AI agents for Security Copilot.
- Further developments in AI encompass AI model releases by Alibaba, Amazon, Databricks, and enhanced business intelligence by AWS QuickSight.
Siliconangle | 29 Mar, 2025
The TAO of data: How Databricks is optimizing AI LLM fine-tuning without data labels
- Labeled data is crucial for training AI models but collecting and curating it can be time-consuming and costly for enterprises.
- Databricks introduced Test-time Adaptive Optimization (TAO) to fine-tune AI models without the need for labeled data, outperforming traditional methods.
- TAO uses reinforcement learning and exploration to optimize models with only example queries, eliminating the need for paired input-output examples.
- The approach includes mechanisms like response generation, reward modeling, and continuous data improvement to enhance model performance.
- TAO utilizes test-time compute during training without increasing the model's inference cost, making it cost-effective for production deployments.
- Databricks' research shows that TAO surpasses traditional fine-tuning methods in terms of performance while requiring less human effort.
- TAO has demonstrated significant performance improvements on enterprise benchmarks, approaching the capabilities of more expensive models like GPT-4.
- By enabling the deployment of more efficient models with comparable performance and reducing labeling costs, TAO offers a compelling value proposition.
- The time-saving element of TAO accelerates AI initiatives by eliminating the lengthy process of collecting and labeling data, thus expediting time-to-market.
- Organizations with limited resources for manual labeling but a wealth of unstructured data stand to benefit the most from TAO's capabilities.
VentureBeat | 28 Mar, 2025
The AI Hype Trap: Why Startup Culture Needs More Substance and Less Speculation
- AI startups experienced unprecedented funding in 2024, with companies like Databricks raising $10 billion at a $62 billion valuation.
- The culture behind AI startups and the investors backing them often mirror the hype they create, which poses a significant risk.
- Examples like Nikola Corporation and LightSail Energy highlight the failure and bankruptcy risks associated with hype-driven culture.
- To increase success rates, startup culture needs more substance, humility, and focus on building strong teams and internal culture.
Medium | 28 Mar, 2025
Databricks and Anthropic Partner to Bring AI Models to Businesses
- Databricks and Anthropic have partnered to integrate AI models into the Databricks data intelligence platform.
- This collaboration aims to enable over 10,000 businesses to build and deploy AI agents using their own data.
- The partnership allows companies to use Anthropic's latest AI model, Claude 3.7 Sonnet, directly within Databricks on cloud platforms like AWS, Azure, and Google Cloud.
- The integration will help businesses analyze large datasets, automate processes, improve decision-making, and focus on responsible AI development.
Analyticsindiamag | 27 Mar, 2025
Ensuring Data Quality With Great Expectations and Databricks
- Great Expectations is a popular open-source data quality and testing framework that helps data teams to define, document, and monitor data quality expectations for their datasets.
- Integrating Great Expectations with Databricks allows you to automate data quality checks within your Databricks workflows, ensuring that your data is accurate, consistent, and reliable.
- Great Expectations can be used with a wide variety of data platforms, including relational databases, data warehouses, data lakes, file systems, and big data platforms like Apache Spark and Databricks.
- By following the steps outlined in the article, you can create, validate, save, and load expectations for your data and generate data documentation to visualize the validation results, ensuring data quality and reliability in your data pipelines.
Dzone | 27 Mar, 2025
Unified data intelligence: Google Cloud and Databricks fuel AI innovation
- Databricks and Google Cloud collaboration aims to democratize data and AI through a unified data intelligence platform that scales AI and analytics efficiently.
- The partnership between Databricks and Google Cloud Marketplace facilitates businesses to adopt AI and analytics solutions without heavy infrastructure overhead.
- Databricks' evolution includes Unity Catalog for centralized governance and integration with Google Cloud to connect with Google's data ecosystem seamlessly.
- Databricks launched a fully containerized deployment on Google Cloud, optimizing resource allocation and enhancing cost efficiencies for organizations.
- Technical synergy between Google Cloud and Databricks allows for interoperability with tools like BigQuery and Vertex AI, improving workflow efficiency.
- Companies like Uplight leverage Databricks through Google Cloud Marketplace to process real-time insights and optimize energy use at scale.
- Uplight's AI-driven insights enabled significant energy savings and rapid application deployment during a severe California heat wave in 2022.
- Financial institutions and companies worldwide utilize Databricks for critical applications such as fraud detection and trade settlement, benefiting from unified data intelligence.
- The partnership success is attributed to the optimization of Databricks to run effectively on Google Cloud, enhancing performance and user experience.
- The integration between Databricks and Google Cloud Marketplace showcases the power of unified data intelligence for transforming raw data into valuable insights efficiently.
Siliconangle | 27 Mar, 2025
Anthropic Inks Deal to Bring Its AI Model to Databricks Platform
- AI company Anthropic has partnered with Databricks to bring its AI model to the Databricks Data Intelligence Platform.
- Anthropic's AI models and services, including the Claude model, will be offered to over 1,000 companies through Databricks.
- The collaboration aims to help enterprises build and deploy AI agents that reason over their own data, meeting production-level requirements.
- This partnership comes as Anthropic focuses on enterprise customers and aims to simplify knowledge work using AI technology.
Pymnts | 26 Mar, 2025
Databricks partners with Anthropic and touts breakthrough in reinforcement learning
- Databricks has partnered with Anthropic to integrate Anthropic's language models and services into the Databricks Data Intelligence Platform.
- The partnership allows Databricks' customers to access Claude 3.7 Sonnet, a hybrid reasoning model, within the Databricks ecosystem on various cloud platforms.
- By leveraging Anthropic's models, organizations can securely build and deploy AI agents that utilize proprietary data and comply with data governance and privacy standards.
- Additionally, Databricks has developed a fine-tuning method called Test-time Adaptive Optimization (TAO), which enables faster and cheaper model fine-tuning without the need for expensive labeled data.
Siliconangle | 26 Mar, 2025
Lakehouse: Manus? MCP? Let’s Talk About Lakehouse and AI
- AI has become an unavoidable topic across various industries since the launch of ChatGPT by OpenAI in late 2022, leading many companies to transform into AI companies quickly.
- Databricks, Snowflake, and Elasticsearch have all shifted to AI data platforms or AI-ready data analytics and search products.
- The article explores the relationship between Lakehouse and AI in the data analytics domain, focusing on the Model Context Protocol (MCP) introduced by Anthropic in late 2024.
- MCP serves as a communication protocol between large models and data sources, facilitating easy interaction and collaboration.
- By integrating with MCP, tools like Claude Desktop have enhanced efficiency in working with AI data sources.
- Apache Doris MCP Server allows for direct access and exploration of data stored in Apache Doris, demonstrating the integration of AI models with data services.
- The use of a Data Lake enables seamless collaboration among different compute engines in AI development, ensuring data consistency and real-time access.
- Analytics engines like Apache Doris provide high performance and richer SQL expression capabilities, supporting complex AI scenarios with acceptable user experience.
- In the AI era, open data formats like Iceberg and data APIs play crucial roles in enabling seamless data integration and analysis for AI applications.
- Apache Doris supports both open data formats and APIs, positioning itself as a leading data analytics engine for AI applications.
- Future articles will delve into more features of Lakehouse architecture, real-time data warehouses, and query engines like Doris in supporting AI applications and data analysis.
Dzone | 26 Mar, 2025
Billion-Dollar Transactions Drive Global AI Funding to New Heights
- In 2024, AI venture funding exceeded US$100 billion for the first time, with mega-rounds accounting for 69% of total funding.
- Major deals included Databricks raising US$10 billion, OpenAI securing US$6.6 billion, xAI raising US$6 billion, and Anthropic receiving US$4 billion.
- Tech giants like Google, Nvidia, and Qualcomm were active investors in AI startups during Q4 2024, indicating the strategic importance of these investments.
- Startup valuations surged due to VC funding, with 32 AI startups among the 71 new tech unicorns in 2024.
- Europe, while still nascent in AI compared to the US, showed strong potential with high Mosaic scores and growing M&A activity.
- European AI funding rose by 38% in 2024 to US$10.9 billion, with notable rounds going to companies like Wayve, Mistral AI, and Poolside.
- ANYbotics in Switzerland raised US$60 million for global scaling, ranking as the 10th largest AI round in Q4 2024.
- Europe-based AI startups represented over a third of AI M&A transactions in 2024, with the UK leading M&A activity followed by Germany and France.
- Overall, AI funding in Europe saw a significant increase in 2024, with deal counts rising from 943 to 1,030 transactions.
- The rise of AI funding globally and in Europe indicates a strong belief in the economic potential and growth of the AI industry.
Fintechnews | 25 Mar, 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 2.4 out of 5 based on 10+ employee reviews on AmbitionBox. 68% 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 2.6. 68% 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 cons of working in Databricks?
Working at Databricks does have some drawbacks that potential employees should consider. The company is poorly rated for job security, company culture and work life balance, based on 10+ employee reviews on AmbitionBox.
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