Data Engineering Intern

6d6 days ago

Rocket Lawyer

San Francisco, US · Internship · $80,000 – $100,000

About this role

Rocket Lawyer is the largest online legal service platform, founded in 2008, helping over 30 million people create legal documents worldwide. We are expanding our team to scale the platform globally. Join as a forward-thinking Data Engineering Intern at the intersection of core infrastructure and AI.

This role involves building robust ETL processes while leveraging AI to automate workflows you create. Support Business Intelligence efforts and influence the product roadmap by turning raw data into actionable insights. Your dual-purpose mission drives advanced dashboards and automated workflows.

Design and maintain ETL pipelines using Python, SQL, and Airflow into Snowflake. Implement AI-driven automation for code generation, data quality checks, anomaly detection, and self-healing pipelines. Create Tableau dashboards, Jupyter/Streamlit tools, and collaborate in agile Scrum with Jira.

Interact with cloud services via CLI, manage Docker and Kubernetes environments. Work closely with product owners and QA to promote code to production. Participate in daily huddles within our growing, global team.

Over the 10-week summer internship, rethink data pipelines and enhance reliability with AI technologies. Contribute to capturing worldwide audiences through data-driven decisions.

Requirements

Currently pursuing an undergraduate degree with targeted graduation in 2026 or early 2027
Pursuing a degree in Computer Science, Data Science, or related quantitative field
Expertise in Python and SQL
Strong understanding of Data Warehousing (Snowflake) and ETL orchestration (Airflow)
Familiarity with CLI, Docker, and Kubernetes for cloud-based environments
Experience with Jupyter Notebooks, Tableau, or Streamlit
Proactive approach to using AI/LLMs to automate repetitive tasks and improve system reliability

Responsibilities

Design, develop, and maintain ETL pipelines to ingest data into Snowflake warehouse using Python, SQL, and Airflow
Implement AI-powered solutions to automate code generation, documentation, data quality checks, anomaly detection, and self-healing pipelines
Use Jupyter Notebooks and Streamlit to analyze data and build internal tools for product team decisions
Create high-impact dashboards in Tableau to translate complex data for stakeholders
Participate in daily Scrum huddles, manage tasks via Jira, and collaborate with product owners and QA to promote code to production
Interact with cloud services via CLI and manage containerized environments using Docker and Kubernetes