Data Engineer • Mid-Level

Who you are

You have a strong background in data engineering, with experience in designing and maintaining scalable data pipelines. You are proficient in Python and have worked with distributed processing frameworks, particularly on platforms like Databricks. Your understanding of both structured and unstructured data allows you to effectively ingest, normalize, and enrich large volumes of data from various sources.

You thrive in collaborative environments, working closely with AI engineers, data scientists, and software engineers to define data schemas, interfaces, and APIs that support model development and production services. Your attention to detail ensures that you implement automated data validation, testing, monitoring, and alerting to maintain data quality and freshness.

What you'll do

In this role, you will design, build, and maintain scalable data pipelines and ETL processes using Python and Databricks. You will be responsible for ingesting and processing large volumes of data, including market feeds and vendor datasets, ensuring that the data is normalized and enriched for analysis. Collaborating with cross-functional teams, you will define data schemas and interfaces that facilitate model development.

You will implement automated data validation and monitoring processes to ensure data quality and detect any drift in data over time. Your role will also involve optimizing pipeline performance and managing cloud resources efficiently to reduce costs. Additionally, you will enforce data governance and privacy standards while maintaining metadata and end-to-end data lineage using data cataloging tools.

What we offer

At Point72, you will be part of a team that is at the forefront of AI solutions in investment services. We are committed to fostering a culture that values innovation and collaboration. You will have opportunities for professional growth and development, working alongside some of the brightest minds in the industry. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.