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Senior Data Engineer

Robinhood

Menlo Park, California, United StatesOn Site

Original Job Summary

Overview

Join us at Robinhood in building the future of finance. Our mission is to democratize finance for all, empowering the next generation to manage an estimated $124 trillion of inherited assets.

About the Team & Role

As a Senior Data Engineer, you will be part of an elite team tackling some of the world’s largest financial challenges using frontier technologies. Robinhood is a metrics-driven company where data underpins key decisions ranging from growth strategy to product optimization.

Key Responsibilities

  • Define and build key datasets across all product areas.
  • Build scalable data pipelines using Python, Spark, and Airflow.
  • Collaborate with engineering, data science, and business teams.
  • Design intuitive data models based on consumption patterns.
  • Promote data engineering best practices across the company.

Required Qualifications

  • 5+ years of professional experience building end-to-end data pipelines.
  • Expertise in Python, Spark (or similar frameworks), and SQL.
  • Experience with open source frameworks such as Flink is a plus.
  • Ability to solve cross-data stack problems and empower data democratization.

What We Offer

Competitive compensation, comprehensive health benefits, wellness allowances, mental health support, generous time off, catered meals, and commuter benefits. Base pay is determined by location, with detailed ranges provided for specific zones.

Key skills/competency

  • Data Engineering
  • Python
  • Spark
  • Airflow
  • SQL
  • Data Pipelines
  • Data Modeling
  • Collaboration
  • Analytics
  • Data Infrastructure

How to Get Hired at Robinhood

🎯 Tips for Getting Hired

  • Research Robinhood's culture: Understand their mission, values, and recent initiatives.
  • Customize your resume: Highlight data pipeline and Python expertise.
  • Showcase technical skills: Emphasize projects with Spark and Airflow.
  • Prepare for technical interviews: Practice SQL and system design scenarios.

📝 Interview Preparation Advice

Technical Preparation

Review Python coding challenges in data engineering.
Practice Spark and SQL queries.
Study Airflow pipeline examples.
Examine large-scale dataset architectures.

Behavioral Questions

Describe handling cross-team conflicts.
Explain a challenging data project experience.
Discuss adapting to fast-paced changes.
Describe how you solve complex problems.