Its values, its interview process and the questions it asks, in short episodes. Listen on a walk, then practice.
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This module synthesizes everything from the series into a complete framework for answering the most important behavioral question: "Why do you want to work at Databricks?" Candidates will learn how to construct a compelling, specific answer that weaves together the company's mission, technical challenges, culture, and their personal career story -- moving far beyond generic responses.
This module provides a stage-by-stage breakdown of the Databricks interview process based on candidate reports from Glassdoor, Blind, interviewing.io, and other sources. Candidates will learn the exact structure, timing, question types, and evaluation criteria for each round, including specific behavioral questions that have been reported and how the hiring committee makes decisions.
This module examines Databricks' leadership approach -- from co-founder involvement in daily operations to the hybrid work model and team structure. Candidates will understand the management culture, what it is like to work at Databricks day-to-day, and how to demonstrate leadership alignment in behavioral interviews, whether they are applying for an IC or management role.
This module covers the 15+ major product announcements from Databricks' Data+AI Summit 2025 and subsequent launches through early 2026. Candidates will learn about Databricks One, Lakeflow, serverless GPU compute, and Unity Catalog enhancements -- demonstrating they track what the company is building right now, not just its history.
This module decodes Databricks' five culture principles -- customer obsessed, truth seeking, operate from first principles, bias for action, and put the company first -- and explains how each directly maps to behavioral interview evaluation criteria. Candidates will learn what these principles mean in practice and how to craft STAR stories that demonstrate alignment with each one.
This module explores Databricks' "compound AI systems" philosophy, which argues that the future of AI is not single monolithic models but orchestrated systems of multiple specialized components. Covering Mosaic AI, Agent Bricks, Genie, and the MosaicML acquisition, this module equips candidates to discuss Databricks' technical direction with credibility in engineering and system design interviews.
This module examines Databricks' open-source-first philosophy, covering their major contributions to Apache Spark, Delta Lake, MLflow, and Unity Catalog. Candidates will understand how open source is not just a technical choice but a core business strategy, and how to connect this philosophy to their own experiences with community-driven development in interviews.
This module maps Databricks' competitive landscape against Snowflake, Google BigQuery, and traditional database vendors like Oracle. Candidates will learn the key differentiators -- open-source flexibility, multi-cloud support, and AI-native architecture -- that distinguish Databricks and help them articulate an informed answer to "Why Databricks over competitors?"
This module covers Databricks' current product strategy, including the three growth pillars CEO Ali Ghodsi outlined for reaching a trillion-dollar valuation: the transactional database market (Lakebase), AI-powered coding ("vibe coding"), and enterprise AI agents (Agent Bricks). Candidates will learn the specific products driving growth and how to articulate why Databricks' strategic direction excites them.
This module traces Databricks from its origins in UC Berkeley's AMPLab, where seven co-founders created Apache Spark, to its current position as a $134 billion data and AI platform company. Candidates will understand the academic roots, the open-source-first philosophy, and how the "lakehouse" concept emerged as the company's defining architectural bet -- essential context for answering "Why Databricks?" with genuine depth.