Databricks

About Databricks

Empowering data teams with unified analytics

🏢 Tech👥 1K-5K📅 Founded 2013📍 San Francisco, California, United States

Key Highlights

  • Headquartered in San Francisco, CA
  • Valuation of $43 billion with $3.5 billion raised
  • Serves over 7,000 customers including Comcast and Shell
  • Utilizes Apache Spark for big data processing

Databricks, headquartered in San Francisco, California, is a unified data analytics platform that simplifies data engineering and collaborative data science. Trusted by over 7,000 organizations, including Fortune 500 companies like Comcast and Shell, Databricks has raised $3.5 billion in funding, ac...

🎁 Benefits

Databricks offers competitive salaries, equity options, generous PTO policies, and a remote-friendly work environment. Employees also benefit from a l...

🌟 Culture

Databricks fosters a culture of innovation with a strong emphasis on data-driven decision-making. The company values collaboration across teams and en...

Databricks

Software Engineering Entry-Level

DatabricksMountain View - On-Site

Posted 1d ago🏛️ On-SiteEntry-LevelSoftware Engineering📍 Mountain View💰 $150,000 - $190,000 / yearly
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Overview

Databricks is hiring a Software Engineer for their Delta Live Tables team, focusing on building and managing data pipelines. You'll work with technologies like Apache Spark and Python. This position is ideal for recent PhD graduates.

Job Description

Who you are

You are a recent PhD graduate with a strong foundation in data engineering and a passion for simplifying complex data processes. Your academic background has equipped you with the skills to tackle challenges in building and managing reliable data pipelines. You have a solid understanding of ETL processes and are eager to apply your knowledge in a practical setting. You thrive in collaborative environments and are excited about contributing to innovative solutions that enhance data lifecycle management.

You possess a deep understanding of data processing frameworks, particularly Apache Spark, and are familiar with the principles of declarative pipeline development. Your analytical mindset allows you to optimize data workflows and ensure high-quality data delivery. You are comfortable working with large datasets and have experience in monitoring and recovery processes within data engineering contexts. You are eager to learn and grow in a fast-paced environment, contributing to a team that values creativity and technical excellence.

What you'll do

As a Software Engineer on the Delta Live Tables team, you will design and implement robust data pipelines that facilitate both batch and streaming data processing. You will leverage your expertise in Apache Spark to optimize pipeline execution and enhance the overall performance of data workflows. Your role will involve collaborating with data engineering teams to simplify ETL development and management, ensuring that data quality is maintained throughout the lifecycle.

You will be responsible for developing features that improve the functionality of the Delta Live Tables platform, including automatic data testing and deep visibility for monitoring. Your contributions will directly impact the efficiency of data processing for over 10,000 customers, as you work to streamline the ingestion and transformation of data across various cloud environments. You will also have the opportunity to engage in research and development initiatives aimed at advancing the capabilities of the Databricks Lakehouse Platform.

What we offer

At Databricks, you will be part of a dynamic team that is at the forefront of data innovation. We offer a collaborative work environment where your ideas and contributions are valued. You will have access to cutting-edge technologies and the opportunity to work on projects that have a significant impact on the industry. We provide competitive compensation and benefits, along with opportunities for professional growth and development. Join us in our mission to simplify the data lifecycle and empower organizations to harness the full potential of their data.

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