Databricks

Databricks built the data lakehouse before it had that name, and they've been running hard since. They're the company behind Apache Spark and Delta Lake — open source projects that underpin a huge fraction of the world's data infrastructure. The commercial platform on top of those projects has grown into one of the most successful enterprise software businesses of the last decade.

If you care about data, AI infrastructure, or large-scale distributed systems — and want to work at a company that's genuinely technical at its core — Databricks is a serious option.

Who they're hiring

Databricks hires across engineering, product, data science, field engineering, and sales. On the engineering side:

• Platform/runtime engineering — the core Databricks platform, Delta Lake, MLflow, and the underlying compute infrastructure
• AI/ML engineering — LLMs, foundation model training, DBRX, and the AI features going into the product
• Product engineering — the workspace, notebooks, jobs, and the features customers use day to day
• Field engineering / solutions architects — a large and important function at Databricks, helping enterprise customers actually deploy and get value from the platform

Research is a real function too — Databricks Research has published serious work in distributed ML, data systems, and AI. For people with strong research backgrounds, there are paths here that exist at very few other product companies.

The process

The process varies significantly by role:

For software engineering:

1. Recruiter screen
2. Technical screen — coding (often using Python, Scala, or Java — know which the team uses)
3. System design interview — usually focused on distributed data systems, not generic web app design
4. Onsite — 4-5 interviews covering coding, distributed systems design, domain knowledge, and behavioral

For research roles:

The process is more academic: expect a research presentation, deep technical discussion, and evaluation of published or demonstrable work.

For field engineering / solutions architects:

More technical discovery-oriented: can you understand a complex customer architecture, identify the right Databricks components, and articulate tradeoffs? There's usually a technical case and a customer scenario.

The bar for all roles is high. Databricks competes with Google (BigQuery), Snowflake, and AWS for enterprise data dollars, and the team they hire reflects that competitive context.

What the culture is actually like

Databricks has a distinctive culture that comes from its academic roots. The founders came out of the AMPLab at Berkeley, and that influences how the company operates: there's a premium placed on technical rigor, published research, and deep expertise. It doesn't feel like a traditional startup or a FAANG — it feels like a place where engineers have opinions about systems design and aren't shy about expressing them.

The pace is fast and the company is large (several thousand employees), but the culture has maintained more of the technical edge than many companies at this scale. There are still active researchers, the open source work is still central, and the engineering culture is driven by people who have been working on this problem for years.

Enterprise sales is a huge part of the business, which means there's organizational weight behind the GTM motion. If you're in engineering, you'll interact with the needs of large enterprise customers. Some people find this grounding and motivating; others find it constraining.

What they look for

Distributed systems fluency. For most engineering roles, you need genuine depth in distributed systems. Understanding consistency, fault tolerance, and the tradeoffs in systems like Spark or Delta Lake isn't optional — it's table stakes for most of the engineering org.

Data domain knowledge. People who understand data pipelines, data warehousing, streaming systems, or ML infrastructure fit naturally. You don't have to be a data engineer to join, but curiosity about the data layer is helpful.

Research orientation (for the right roles). Databricks values people who can contribute to the state of the art — not just implement what exists. For research and senior engineering roles, publication history or equivalent demonstrable depth matters.

Collaboration across technical and customer contexts. The enterprise nature of the business means that a lot of engineering ends up intersecting with customer needs. Engineers who can engage with that complexity — understanding what customers actually need and why — do well.

The AI moment

Databricks has made a major push into AI. DBRX was their open-source LLM. They acquired MosaicML. They've integrated AI features throughout the platform. This is a real area of investment and a real hiring priority.

If you're in AI/ML — whether research, infrastructure, or product integration — Databricks is one of the most interesting enterprise companies to consider. They're building AI on top of real data infrastructure, which is where most serious enterprise AI applications will live.

Things worth knowing

The IPO conversation is ongoing. Databricks has been one of the most anticipated IPOs in enterprise software. They've been profitable at scale and have been pushing toward a public market listing. The timing is uncertain, but the equity story for current employees is real and well-understood.

Scala is a real thing here. The core platform is built on Scala (because Spark is Scala). If you're a Java or backend engineer who has avoided Scala, some roles will require you to get comfortable with it. Python is increasingly the language of choice for higher-level work, but knowing Scala helps.

The open source DNA matters. Databricks engineers are often working on software that will eventually become open source — or that is already open source. If you care about community, contribution, and impact beyond just one company's customers, the Databricks approach is distinctive.

Office vs. remote. Databricks has offices in SF, Seattle, Amsterdam, London, and others. Some teams are in-office, some are distributed. Ask specifically about your team's arrangement — it varies significantly.

Should you apply?

If you work in data engineering, ML infrastructure, distributed systems, or enterprise AI — Databricks is one of the clearest choices in the market. The technical depth of the company is real, the business is strong, and the IPO path is visible. If you want to be at the intersection of open source, enterprise, and cutting-edge AI infrastructure, this is a serious option.