Data Engineering Research Assistant — Python & ETL Pipelines

UM01 University of Maryland College Park (UMCP); United States; Sur place; USD 52 000 - 70 000; Plein temps

Résumé du poste

The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is seeking a Data Engineering Research Assistant to help build Python-based data pipelines, validate data, and support search and retrieval components under senior staff guidance.

Responsibilities include preparing data in databases, loading pipelines, and ensuring tests with Pytest, while collaborating with researchers, engineers, and analysts to deliver reliable data solutions.

Qualifications

• Proficient in Python programming and data pipeline development.
• Experience with ETL/ELT design, data transformation, and loading into databases.
• Hands-on experience with relational or document databases, schema design, and data validation.
• Familiarity with Linux command line and Git-based workflows (branches, PRs).
• Experience writing and running tests in Python (Pytest).
• Knowledge of containerization using Docker for app run/troubleshooting.

Responsabilités

• Build and run Python-based data pipelines
• Prepare, load, and validate data in databases
• Assist in implementing search and retrieval components under senior guidance

Connaissances

Python programming

ETL/ELT design

Database design and querying

Linux command line

Git workflow

Testing in Python (Pytest)

Formation

Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, or a related field

Outils

Docker

Git

Pytest

Description du poste

The Applied Research Laboratory for Intelligence & Security (ARLIS) at the University of Maryland is seeking a Data Engineering Research Assistant to help build Python-based data pipelines, validate data, and support search and retrieval components under senior staff guidance.

Responsibilities include preparing data in databases, loading pipelines, and ensuring tests with Pytest, while collaborating with researchers, engineers, and analysts to deliver reliable data solutions.