Databricks

What Databricks does

Databricks is the data-and-AI platform company built around the "lakehouse" architecture, and it is the steward of major open-source projects including Apache Spark, Delta Lake and MLflow. Its founders are all PhD engineers — Ali Ghodsi, Matei Zaharia and others — and reviewers note they remain "very involved" in the technical direction. By 2026 the platform had layered generative AI directly on top of the data stack: Mosaic AI, from the 2023 MosaicML acquisition, handles foundation-model training and serving, and in June 2026 the company launched Genie One, Genie Agents and Genie Ontology — described as a "data-smart AI coworker, grounded in enterprise context" — plus Lakehouse//RT and CustomerLake. The 2026 differentiators, per outside reviews, are Lakebase and Unity Catalog. For engineers, the appeal is working on infrastructure that sits at the center of enterprise data and AI, with a culture that famously insists on "first show me the data" before decisions get made.

What it is like to work at Databricks

The engineering bar is the defining feature, and reviewers describe it as extremely high. TrueUp gives the company a Trajectory Score of 97/100. Glassdoor rates it around 3.4/5 for work-life balance, 3.9/5 for culture and values, and 3.9/5 for career opportunities. Employee verdicts collected on Taro include "fun, energetic, and positive work environment; flexible work/life balance (in the field, at least); good remuneration; lots of technically challenging work." A first-year reflection from an engineer describes ICs spending the year "writing PRDs and leading the work of ~15-25 engineers," which signals real ownership and scope early. The culture is product-driven rather than pure infrastructure, and it leans on in-office presence. The overall picture is a high-rigor, high-challenge environment where strong engineers get meaningful scope, balanced against a demanding bar and an expectation of office attendance.

What Databricks pays

Databricks pays competitively and pairs cash with pre-IPO equity that many employees value highly given the company's trajectory. Reviewers on Taro cite "good remuneration" alongside the technically challenging work, and the equity narrative is anchored by an aggressive valuation climb: the Series K was signed at over $100 billion on August 19, 2025, and a $4 billion Series L in December 2025 brought the pre-money valuation to $134 billion. With the company widely seen as on the IPO horizon, employee equity carries a clear potential liquidity path, which is a major part of the pitch. The practical read for candidates is that total compensation is strong and heavily influenced by where the eventual IPO prices, so the paper value of a grant should be weighed against both the private valuation and the still-uncertain timing of a public listing.

How hiring works at Databricks

The process follows a rigorous, FAANG-style structure with a deep data emphasis. Interviewing.io and Exponent document four steps: a roughly 30-minute recruiter screen, a one-hour technical phone screen, a hiring-manager round, and onsite loops combining one behavioral round with multiple technical rounds. Junior candidates sometimes ask whether LeetCode is required; the consensus from a Databricks hiring megathread is that the bar is "extremely high" and the process is "intense," with strong emphasis on Spark and Delta internals for relevant roles. Because the company is hiring aggressively — 815-plus open roles as of May 2026, weighted toward AI/ML — the pipeline is active, but the standard does not soften with volume. Candidates should prepare for a demanding technical loop and expect the data-platform depth to matter as much as general coding ability.

Growth & trajectory

Databricks is scaling fast and heading toward an IPO. Headcount sits at roughly 12,000 to 15,000 as of May 2026, up about 11.3% year-to-date and around 24% year-over-year. The capital story is steep: a Series K signed at over $100 billion in August 2025, followed by a $4 billion Series L in December 2025 that lifted the pre-money valuation to $134 billion. With 815-plus open roles as of May 2026, hiring velocity is high and concentrated in AI/ML. The generative-AI layer built on the MosaicML acquisition is both a moat — a one-stop AI-plus-data platform — and a target that invites competition. For an engineer, the trajectory means abundant opportunity and IPO-timed equity upside, set against a maturing organization that is still building the structures of a much larger public company.

Risks to know

The main risks are competitive and structural. Snowflake is a direct and formidable competitor, and the Mosaic AI acquisition that integrated generative AI into the platform makes Databricks both a stronger one-stop shop and a bigger target. Going public would unwind private-equity gains and reprice employee equity, so the IPO is both the upside catalyst and a source of valuation uncertainty. On the work side, Glassdoor's roughly 3.4/5 work-life-balance rating signals a demanding pace, and the remote-work policy is hybrid-first: an April 2026 report describes badge-tracking that enforces three days in-office, which is a hard constraint for anyone wanting remote-only work. No major leadership departures were reported in the 2024-2026 window, which is a point of stability. Overall the risks are less about instability and more about competitive intensity, in-office requirements, and equity value that hinges on IPO timing and pricing.

Who thrives at Databricks (and who should not)

Thrives: PhD-leaning engineers with data-platform pedigree, people who enjoy a product-driven environment with real ownership over PRDs and cross-team work, engineers comfortable with onsite presence at the SF headquarters, and anyone who wants to ride the IPO wave on strong equity. The culture rewards data-first rigor and technical depth in Spark, Delta and the broader lakehouse stack. Should not join: anyone who wants a remote-only lifestyle, since badge-tracking enforces three days in-office per the 2026 policy, and engineers who prefer a pure-infrastructure setting over a product-driven one. The bar is high enough that candidates who are not prepared for an intense, data-heavy interview loop and a demanding pace will struggle. In short, Databricks suits rigorous, hybrid-comfortable data engineers who want scope and IPO-timed upside more than it suits remote-first or purist-infrastructure profiles.

Roles Databricks is hiring for

The roles Databricks is most actively hiring right now in our index, with a live count and the salary guide for each:

The full board of open roles — with comp and location on every posting — is at the top of this page.

The signals behind this page

The hiring picture here is read from 549 live Databricks postings in our index (refreshed weekly); 100% are remote-friendly, and in a recent sample 0 disclose a pay-transparency band. The culture, growth, and interview detail above is researched and cited; the open-roles board is live from our jobs index.

Sources

• Databricks — Series K at >$100B valuation (Aug 19, 2025)
• Databricks Blog — Genie One / Genie Agents launch (Jun 16, 2026)
• Reddit — Databricks hiring & interviewing megathread (Mar 2025)
• Greg Kroleski — Reflecting on my first year at Databricks (Sep 2023)
• jobsbyculture — Databricks remote work policy 2026
• unifygtm — Databricks headcount trends
• TrueUp — Databricks company profile
• Glassdoor — Databricks reviews
• interviewing.io — Databricks interview questions