About the team + role

The Analytics Engineering team within Robinhood’s Data Science organization plays a critical role in transforming data into strategic insights. We build foundational data products that support product development, regulatory compliance, and operational efficiency. Our mission is to empower teams across Robinhood with trustworthy, scalable, and self-serve analytics infrastructure. We collaborate closely with product managers, engineers, and data scientists to ensure data is accessible, accurate, and actionable.

As an Analytics Engineer, you will lead the development of high-performance ETL pipelines, tools, and dashboards that power decision-making across the company. You’ll partner with cross-functional stakeholders to define metrics, curate datasets, and surface insights that inform everything from product strategy to regulatory reporting. This role offers the opportunity to shape the company’s data foundations and have a long-lasting impact on Robinhood’s growth.

The role is located in the office location(s) listed on this job description which will align with our in-office working environment. Please connect with your recruiter for more information regarding our in-office philosophy and expectations.

What you’ll do

• Collaborate with cross-functional teams (e.g., PeopleX, engineering, and data science) to support analytics projects, including building dashboards, defining metrics, and delivering recurring reports.
• Assist in developing and maintaining reliable ETL pipelines and data models using established data tooling (e.g., Airflow, Spark), under guidance from senior team members.
• Contribute to the development and refinement of internal dashboards and reporting tools to support operational and business decision-making.
• Support data quality initiatives by monitoring pipeline performance and helping troubleshoot issues with guidance from more senior engineers.
• Work alongside data scientists and analysts to translate raw data into clean, usable datasets for analysis and reporting.
• Participate in team efforts to improve data documentation, promote analytics best practices, and support a self-serve data culture across stakeholder groups.

What you bring

• At least

  1 year of experience

  (including internships or professional settings) in Analytics Engineering or Data Engineering.

• Working knowledge of SQL

  and

  basic Python scripting

  for data analysis and transformation; exposure to Apache Spark (PySpark, Spark SQL) is a plus.

• Some experience

  assisting in the development or maintenance of ETL pipelines, ideally using tools like Airflow or similar workflow orchestration frameworks.
• Exposure to

  dashboarding tools

  such as Superset, Looker, or Tableau, with a focus on usability and performance.
• Familiarity with

  version control systems

  (e.g., Git/GitHub) and an understanding of modern data development workflows.
• A

  curious and product-oriented mindset

  , with an eagerness to understand business needs and contribute to data-driven solutions.
• Comfortable working in a

  collaborative, fast-paced environment

  , and receptive to feedback and mentorship from more experienced teammates.

What we offer

• Market competitive and pay equity-focused compensation structure
• 100% paid health insurance for employees with 90% coverage for dependents
• Annual lifestyle wallet for personal wellness, learning and development, and more!
• Lifetime maximum benefit for family forming and fertility benefits
• Dedicated mental health support for employees and eligible dependents
• Generous time away including company holidays, paid time off, sick time, parental leave, and more!
• Lively office environment with catered meals, fully stocked kitchens, and geo-specific commuter benefits