Data Engineer • Entry-Level

Who you are

You are an entry-level data engineer with a foundational understanding of Python and data management principles. You have a keen interest in working with big data and are eager to learn about building data platforms that support quantitative research. You thrive in collaborative environments and are excited about the opportunity to contribute to a new team focused on data engineering.

You possess strong analytical skills and are comfortable working with both structured and unstructured data. You are detail-oriented and have a passion for problem-solving, which will help you in validating and provisioning large datasets. You are also open to feedback and eager to grow your technical skills in a supportive environment.

What you'll do

In this role, you will participate in the design and development of a new enterprise data platform that will be utilized by quantitative researchers at AQR. You will work closely with team members to load, validate, and manage large datasets that are critical for research and investment processes. Your responsibilities will include building tools for data validation and back-testing, ensuring that the data infrastructure is robust and reliable.

You will collaborate with researchers and other stakeholders to understand their data needs and help implement solutions that enhance their ability to analyze market trends. As part of a new team, you will have the opportunity to contribute ideas and innovations that can transform the way AQR approaches data management.

What we offer

AQR Capital Management offers a dynamic work environment where you can grow your skills and advance your career in data engineering. You will be part of a team that values excellence in technology and encourages innovative thinking. We provide opportunities for professional development and mentorship, ensuring that you have the support you need to succeed in your role. Join us and be part of a firm that is committed to delivering superior results for our clients through a systematic, research-driven approach.