Internal Audit Analytics Intern

at Coinbase

📍 New York City, United States

USD 50 per hour

INTERN

✅ Hybrid

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 3 Audit Compliance Data Science @ 3 ETL @ 2 GenAI Generative AI @ 3 SQL @ 5 Snowflake Statistics @ 3

Details

This is a 12-week internship during summer 2027. The role supports the Internal Audit Data Analytics Team with data-driven testing of controls across Coinbase's global technology infrastructure and crypto-native products. Responsibilities include extracting, analyzing, and validating datasets from internal data systems to assess control design and operational effectiveness, translating audit data requests into SQL-based extracts, and producing actionable findings that support audit control testing objectives.

Responsibilities

Write SQL queries against Snowflake and other internal data platforms to assess the design and operational effectiveness of key internal controls.
Build continuous auditing processes and AI tooling for use throughout Internal Audit.
Test system and model changes by validating compliance and performance impact, and document results for relevant stakeholders.
Produce data extracts and reports from a variety of sources, translating audit data requests into clear, actionable requirements and outputs.
Validate data ETL processes from upstream production systems into the data repository and from the repository into enterprise tools, checking for completeness and accuracy at each stage.

Requirements

Currently pursuing a degree in Data Science, Computer Science, Statistics, or a related field.
Demonstrated SQL proficiency, including experience writing queries and working with large datasets through coursework, projects, or prior internships.
Familiarity with data ETL concepts, including how data moves between production systems, repositories, and enterprise tools.
Experience presenting data findings to non-technical audiences through written reports, presentations, or project deliverables.
Responsible use of generative AI, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.