Data Engineering Intern

The Data Engineering Intern will join EQT's Completions Services team for a summer internship, gaining hands-on exposure to how the team manages and works with operational data. Working alongside the team's engineers, the intern will build foundational data engineering skills — SQL, Python, data engineering, and analytical concepts — while contributing to real, scoped project work that supports Completions day-to-day operations.

The Data Engineering Intern's responsibilities include but are not limited to:

• Assist with SQL queries and Python scripts that support Completions Services data workflows.
• Support the team's engineers on scoped tasks across the data lifecycle: data pulls, validation, cleanup, and basic documentation.
• Learn and follow the team's coding, documentation, and data quality standards.
• Assist with light code review and testing on assigned tasks, under supervision.
• Help document scripts, workflows, and data definitions for team reference.
• Gain exposure to Databricks SQL and notebooks.
• Support ad-hoc departmental data requests.
• Complete a defined summer project and present a summary of the work and what was learned at the end of the internship.

Required Experience and Skills:

• Currently pursuing an accredited Bachelor's Degree in Computer Engineering, Computer Science and Engineering, Systems Engineering, Industrial Engineering, Data Engineering or a closely related field; rising sophomore standing or above.
• Some coursework or self-directed experience in at least one programming language (Python preferred).
• Basic exposure to SQL or relational databases (coursework, bootcamp, or personal projects are fine).
• Strong problem-solving skills and eagerness to learn new tools.
• Comfortable working in a team environment, asking questions, and taking feedback.
• No professional work experience required.

Preferred Experience and Skills:

• Familiarity with basic SQL syntax (SELECT, JOIN, WHERE, GROUP BY).
• Exposure to Python for scripting or data manipulation (e.g., pandas).
• Interest in the oil & gas or energy industry.
• Exposure to cloud-based data platforms (Databricks, Snowflake, AWS, Azure, or similar).
• Coursework in databases, data structures, or statistics.