As a software engineer, you will work with our engineering team to build infrastructure and products for the Databricks platform. You will join a cohort of engineers who will build their careers through collaborative projects and learning opportunities. With a dedicated mentor and supportive team, you'll onboard quickly and start contributing to impactful projects soon after starting here at Databricks!

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The impact you will have:

Work with a team to build features and improve architecture for the Databricks platform
Develop and extend the Databricks product
Own full software development lifecycle: design, development, testing, and operating in production
Build solutions with a high level of reliability, scalability, and security

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What we look for:

You will graduate between fall 2025 and summer 2026 with a degree in Computer Science, Engineering, or related subject
You have implementation skills with a general purpose program language, such as Python, Java, or C++
You have knowledge of algorithms, data structures, and OOP principles
You have experience managing end-to-end projects

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About the company

Databricks

Company overview

Databricks builds a cloud platform used by data teams to run analytics, data engineering, and AI workloads in one place, often described as a “lakehouse” approach. The company sells to enterprises that want to unify data pipelines, BI, and machine learning on major clouds. Databricks is closely associated with Apache Spark’s origins and supports open-source projects used widely in data engineering and ML workflows. The company’s customers range from large enterprises to fast-growing tech companies building data products.

Locations and presence

Databricks is headquartered in San Francisco and lists many offices across the Americas, EMEA, and Asia-Pacific. The company describes a hybrid setup built around weekly “Team Day” in-office time, with most work happening from home, and notes that some roles are fully remote.

Palpable Score

77.5

/ 100

Databricks offers clear, recurring entry points for students and new grads, and the company backs that up with cohort-style roles and explicit mentorship language in early-career job postings. Databricks also publishes candidate-facing guidance on the hiring process and shares pay bands on at least some early-career roles, which is a practical signal of transparency. The main limiter is mixed public candidate and employee feedback on consistency, especially around responsiveness and clarity of progression, and the lack of published early-career outcomes data.

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