Role — Analytics Engineering Intern

As an Analytics Engineering Intern, you’ll work with the Analytics team to build and maintain
data pipelines, transform large-scale datasets, and enable reliable insights across the company.
This is a hands-on role where you’ll work on real production data and systems used daily by
Product, Engineering, GTM, and Leadership teams.
You’ll be part of a team that handles data at a scale of 5+ PB per month, giving you exposure
to real-world data challenges around performance, reliability, and correctness.

What You’ll Do
● Write production-grade code in Python to build and maintain data pipelines.
● Work with large datasets to ingest, transform, and model data for analytics and reporting
use cases.
● Gain hands-on experience with Snowflake for data warehousing, querying, and
performance optimization.
● Collaborate with Analytics Engineers and Data Analysts to define data models and
metrics.
● Help improve data quality, monitoring, and validation across pipelines.
● Debug pipeline failures, analyze data discrepancies, and help resolve root causes.
● Learn best practices around scalable data processing and analytics engineering.

Who You Are

● Pursuing a Bachelor’s/Master’s degree in Computer Science, Engineering, Statistics, or
a related field.
● Strong fundamentals in Python and data structures.
● Interest in data engineering, analytics, and working with large-scale datasets.
● Basic understanding of SQL and data warehousing concepts.
● Curious mindset with a desire to understand how data flows through systems
end-to-end.
● Comfortable working in a fast-paced, collaborative environment.

Perks & Benefits
● Competitive compensation.
● Wellness days for rest and recharge.
● Flexible work policies to support learning and performance.

What You’ll Gain
● Hands-on experience working with large-scale data pipelines and Snowflake in
production.
● Exposure to real business use cases and decision-making powered by data.
● Mentorship from experienced Analytics Engineers.
● Opportunity to contribute to systems processing 5+ PB of data every month.
● Strong foundation for a future role in analytics engineering, data engineering, or data
science