Mission

We’re looking for a hands-on Data Engineer Summer Intern to work with KDW engineers on building, testing and improving the data infrastructure that powers production analytics and reporting. This is a practical engineering internship — you’ll contribute to real production pipelines, learn the team’s engineering practices (PRs, DORA metrics, sprint rituals), and deliver measurable improvements under engineering mentorship.

The summer internship program runs for 10 weeks from June 3 to August 9. All internship positions are paid. This will be a full-time internship with up to 40 hours a week from Monday through Friday with the expectation of 4x in the office.

What You'll Do

• Develop and maintain data ingestion & transformation pipelines (Python, SQL) for internal reporting and analytics.
• Work with the team to implement and test ETL/ELT jobs (Airflow, Spark/Snowflake or equivalent orchestration/compute).
• Troubleshoot and resolve pipeline failures, contribute to runbook/operational documentation.
• Support identity & data-onboarding work (privacy-aware): help implement identity stitching and 1P data onboarding flows and integrations, and help operationalize privacy guardrails for targeting datasets.
• Author clear, reviewable code and follow PR labeling/commit practices to support team DORA metrics.
• Write and maintain Confluence design docs and Jira ticket updates for work you own or help implement.
• Partner with Analytics and downstream stakeholders (SalesOps, Product) to understand data needs, run validation, and deliver reliable datasets used for business decisions.
• Present a short end-of-internship demo/summary of your project and learnings to the Data team.

What You'll Learn

• How to design, build, and maintain scalable data ingestion and transformation pipelines using Python and SQL for real-world analytics and reporting use cases.
• Hands-on experience implementing and testing ETL/ELT workflows with modern orchestration and compute tools (e.g., Airflow, Spark, Snowflake), including troubleshooting production pipeline issues.
• Best practices for writing clean, reviewable code, contributing to operational documentation, and supporting engineering excellence through structured PR and release processes.
• How identity stitching, first-party data onboarding, and privacy-aware data workflows work in practice—plus how to operationalize guardrails for responsible data usage.
• How to collaborate with Analytics, SalesOps, and Product stakeholders to translate business needs into reliable datasets—and confidently present your work to a technical audience.

Who You Are

• Currently pursuing a BS/MS in Computer Science, Engineering, Data Science, or a related technical field.
• Strong SQL skills and comfort writing queries for data validation and exploration.
• Proficiency in Python and familiarity with software development basics (git, code review).
• Hands-on interest in data engineering topics: ETL/ELT, data modeling, data quality and pipelines.
• Clear written and verbal communication; comfort updating tickets and documenting work in Confluence / Jira.