Software Developer, Data Engineering
Robinhood helps users invest in stocks, ETFs, options, and cryptocurrencies through commission-free trading with no minimum account requirements.
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About Robinhood
Robinhood helps retail investors access financial markets through commission-free trading of stocks, ETFs, options, and cryptocurrencies. With it, users can invest with no account minimums, earn rewards through retirement accounts with matching contributions, and access advanced trading tools. Robinhood democratizes investing by making financial markets accessible to everyone, not just wealthy investors.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and evolve foundational datasets across product areas and create scalable data pipelines that move application data into the data lake. You will work with engineering teams and data consumers to improve data generation, design intuitive data models, establish tooling and standards, and promote data engineering best practices.
Requirements
- Background in computer science or a relevant field of study
- Strong product mindset
- 4+ years of experience with a bachelor's degree, or 2+ years with a master's degree
- Experience building high-quality data solutions
- Strong analytical and problem-solving skills
- Expertise building data pipelines using open-source frameworks such as Hadoop and Spark
- Expertise in one or more programming languages, ideally Python
- Strong SQL skills, including Presto and Spark SQL
- Familiarity with data visualization tools such as Looker and Tableau
- Strong communication skills and ability to provide actionable data insights and solutions
Responsibilities
- Define and build key datasets across product areas
- Evolve datasets as use cases grow
- Build scalable data pipelines using Python, Spark, and Airflow
- Move application data into the data lake
- Partner with engineering teams to improve data generation patterns
- Design intuitive data models based on data-consumption patterns
- Contribute to shared data engineering tooling and standards
- Define and promote data engineering best practices
Benefits
- Bonus opportunities
- Equity