As of 2026, Databricks is actively hiring for 757 open roles, signaling aggressive growth in engineering, sales, and operations. This surge in hiring, with 179 new roles posted in the last 29 days, indicates Databricks's commitment to expanding its capabilities in analytics and AI, making it a competitive player in the market.
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HIRING BY DEPARTMENT, DATABRICKS
══════════════════════════════════════════════════
Engineering ████████████████████ 387
Sales █████░░░░░░░░░░░░░░░ 103
Operations ███░░░░░░░░░░░░░░░░░ 52
Other ██░░░░░░░░░░░░░░░░░░ 41
Product █░░░░░░░░░░░░░░░░░░░ 27
Marketing █░░░░░░░░░░░░░░░░░░░ 25
SENIORITY MIX, DATABRICKS
══════════════════════════════════════════════════
Senior ████████████████████ 317
Staff ██████████░░░░░░░░░░ 166
Mid ████████░░░░░░░░░░░░ 126
Director ██░░░░░░░░░░░░░░░░░░ 37
Principal █░░░░░░░░░░░░░░░░░░░ 15
```
| Metric | Count |
|---|---|
| Open Roles | 757 |
| New Roles in Last 29 days | 179 |
| Top Departments | Engineering (387), Sales (103), Operations (52) |
| Seniority Mix | Senior (317), Staff (166), Mid (126) |
| Top Locations | Mountain View (76), San Francisco (63), Bengaluru (58) |
| Last Refreshed | 2026 |
Databricks's hiring strategy reveals a clear focus on expanding its engineering and sales teams, with a notable emphasis on senior-level positions. The majority of open roles (387) are within engineering, showcasing the company's drive for technical talent to enhance its data platform capabilities. Sales roles also reflect an aggressive push to capture market share, particularly in competitive areas such as AI and data management.
Databricks's hiring trends reveal important insights into its strategic focus and the competitive landscape. With 387 of the 757 open positions in engineering, it’s clear that the company is doubling down on its technical capabilities. This aligns with industry trends we observe-companies that emphasize engineering talent tend to outperform in fast-evolving sectors like AI and analytics. As Elad Gil notes in his book, "the best AI companies don't just screen for skills, they filter for systems thinkers who move fast and build first-principles solutions." This statement rings true for Databricks, where the need for such thinkers is crucial.
The seniority mix within these roles further emphasizes this point. The majority of roles (317) are for senior engineers, indicating Databricks's desire for experienced professionals who can lead projects and drive innovation. Coupled with a significant number of staff (166) and mid-level (126) positions, this suggests a well-thought-out approach to create a balanced team that can mentor junior talent while pushing forward major projects.
Geographically, the hiring is concentrated in tech hubs like Mountain View and San Francisco, which are known for their rich talent pools. This focus on specific locations may be a strategic move to attract the best talent in the field, especially as several of Databricks's competitors, such as Snowflake and dbt Labs, are also based in these regions. The velocity of new roles-179 in just 29 days-signals a rapid expansion that is likely in response to increasing market demands and the competitive landscape. Companies like Snowflake and Monte Carlo are not only competitors for talent but are also pushing the envelope in terms of product offerings and technological advancements, driving Databricks to intensify its hiring efforts.
In summary, Databricks's hiring signals an aggressive strategy aimed at bolstering its engineering and sales capabilities, which is critical in maintaining its competitive edge in a thriving analytics and AI market. Competitors should take note of this hiring trajectory and prepare to adapt their strategies accordingly.
For candidates eyeing opportunities at Databricks, understanding the competitive landscape is crucial. The influx of roles indicates a strong demand for talent, but it also suggests that the bar for entry is high. Databricks is not just looking for technical skills; the company seeks candidates who can thrive in a fast-paced environment and contribute to the innovation that drives its products.
Candidates should be prepared for a rigorous interview process. According to the Ashby 2024 Recruiting Benchmark, top-performing recruiting teams run 2-3 interview stages for senior individual contributors, not the lengthy 5-6 stages that can deter top talent. Databricks's process likely reflects this trend, focusing on efficient yet thorough evaluations to secure top candidates quickly. This speed is essential, as Claire Hughes Johnson from Stripe Press points out: "The highest cost in recruiting isn't the salary, it's a slow process that loses A-players to companies that decide faster."
Furthermore, prospective candidates should weigh the trade-offs of working for Databricks. The company's mission to revolutionize data analytics and AI is compelling, but the intensity of the work environment can be a double-edged sword. Candidates should consider if they are ready for the challenges that come with rapid growth and high expectations.
Location is also a key factor. Many roles are based in tech hubs like San Francisco and Mountain View, which offer vibrant tech ecosystems but also come with high living costs. Candidates need to weigh these aspects against their personal circumstances.
Overall, candidates should approach opportunities at Databricks with a clear understanding of the expectations and the competitive nature of the hiring landscape. The emphasis on senior roles, the efficient interview process, and the high bar for talent all signal that while opportunities abound, they come with significant responsibility and expectation.
If you are a hiring manager or recruiter at companies like Snowflake or dbt Labs, the current hiring climate at Databricks is a significant factor to consider. With 757 open roles, the intensity of competition for talent is at an all-time high. Understanding the specifics of Databricks's hiring strategy can help you shape your own approach to recruiting.
Firstly, competitors should recognize the importance of speed in the hiring process. Databricks's ability to fill roles quickly-with 179 new positions listed in just the last 29 days-demonstrates a commitment to agile hiring practices. Companies that do not match this speed risk losing top talent to Databricks. As Claire Hughes Johnson emphasizes, slow processes can cost companies A-players who might be swayed by faster-moving competitors.
Additionally, the high concentration of senior roles at Databricks indicates a competitive hiring bar. To attract similar talent, competitors should ensure their own offerings are compelling. This can include competitive compensation packages, benefits, and opportunities for professional growth. The salary data from Databricks further shows a median salary of $215K for technical roles, which sets a high standard for compensation in the industry. Competitors must be prepared to meet or exceed these expectations to attract top candidates.
Moreover, the geographical focus of Databricks on tech-centric locations like San Francisco and Mountain View means that competitors need to establish a strong presence in these areas or offer remote work options that are equally attractive. The competition for talent is not limited to technical skills; it extends to company culture, work-life balance, and career advancement opportunities.
In conclusion, competitors must be aware of Databricks's aggressive hiring strategy and adapt their recruitment practices accordingly. By enhancing speed, competitive compensation, and appealing workplace culture, they can position themselves as viable alternatives to Databricks for top talent.
Understanding the origins of Databricks's talent pool can provide valuable insights into the company's hiring strategies and culture. Based on the Recruiting from Scratch Atlas candidate database, we see that Databricks attracts professionals from some of the most competitive companies in the tech industry. Notably, 49 current employees hail from Google, followed by 15 from Microsoft and 13 from Amazon Web Services (AWS). This talent movement indicates a strong preference for candidates who come from established tech giants known for their innovation and technical prowess.
This inflow of talent from top companies signals that Databricks not only values technical expertise but also seeks individuals who have honed their skills in high-pressure environments. The ability to adapt and innovate is crucial in a fast-paced field like data analytics and AI, and candidates from these companies are likely to bring that experience.
The ASCII diagram below visualizes this talent movement:
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TALENT MOVEMENT AT DATABRICKS, Recruiting from Scratch Atlas Database, 2026
═════════════════════════════════════════════════════════════════════
WHERE TALENT COMES FROM │ WHERE TALENT GOES
──────────────────────────────────┼──────────────────────────────────
Google ( 49) │
Microsoft ( 15) │
Amazon Web Services (A ( 13) │
Salesforce ( 10) │
Oracle ( 9) │
──────────────────────────────────┴──────────────────────────────────
200 current + 500 alumni tracked in Recruiting from Scratch Atlas database
```
This movement also highlights the competitive nature of hiring within the tech sector. Companies like Snowflake, dbt Labs, and others are vying for the same talent pool, increasing the pressure on all organizations to refine their hiring practices and company cultures. The presence of alumni from these feeder companies in Databricks's workforce can also enhance collaboration and innovation, as these individuals bring diverse perspectives and experiences to the table.
In summary, the origins of Databricks’s talent provide insights into the company’s high hiring standards and competitive culture. For other companies in the same space, this data signals the need to attract talent from similar sources to keep pace with Databricks's growth and innovation.
At Recruiting from Scratch, we observe several key trends in the current hiring landscape, especially concerning high-growth companies like Databricks. The demand for technical talent remains high, particularly in fields related to AI and data analytics. We see candidates with specialized skills in machine learning and data engineering often receiving multiple offers, which increases the need for companies to act quickly during the hiring process.
Compensation expectations are also a critical factor. The salary data from Databricks indicates a median pay of $215K for technical roles, which sets a standard that candidates will expect across the industry. Companies must be prepared to offer competitive salaries while also highlighting non-monetary benefits such as work-life balance, career growth opportunities, and company culture.
The risk of losing candidates to competitors is a significant concern. With an average time to hire of 29 days at Recruiting from Scratch compared to the industry average of 49 days, we have seen firsthand how speed can impact the recruitment process. Candidates are more likely to accept offers from companies that move quickly and provide clear communication throughout the process.
Our proprietary tools, Atlas and Spyglass, allow us to proactively source talent from a vast database of over 900,000 candidates. This capability gives us a unique advantage in identifying and connecting with the right profiles for our clients, ensuring we can meet the ever-evolving demands of the market.
In conclusion, the current hiring market is highly competitive, and companies must be agile and strategic in their recruitment efforts. By leveraging data-driven insights and prioritizing speed and candidate experience, organizations can successfully navigate the challenges of attracting top talent in a thriving industry.
Databricks is currently hiring for 757 open roles as of 2026, reflecting aggressive growth in multiple departments.
The company is hiring across various departments, with the majority of roles in engineering, followed by sales and operations, indicating a strong focus on technical and customer-facing positions.
While specific information on remote roles isn't detailed, many tech companies, including Databricks, are increasingly offering remote work options to attract a broader talent pool.
Databricks aims to attract senior talent, with 317 senior roles currently open, indicating a high bar for technical expertise and leadership in engineering.
Databricks's hiring strategy is aggressive, with more open roles and a focus on senior-level talent compared to competitors like Snowflake and dbt Labs. Companies must adapt their hiring practices to keep pace with Databricks's growth.
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