Job Description

A Career with Point72’s Data Engineering Technology Team  

As Point72 reimagines the future of investing, our Technology group is constantly improving our company’s IT infrastructure, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts experimenting, discovering new ways to harness the power of open-source solutions, and embracing enterprise agile methodology. We encourage professional development to ensure you bring innovative ideas to our products while satisfying your own intellectual curiosity.

The Data Engineering Technology team provides Point72 with a scalable data engineering capability and set of data services to support the firm’s expanding data needs. We focus on solutions such as cloud computing, data platforms for large scale data processing, data governance, data quality, enterprise reference data, business automation, and high touch service. We have team members across four continents who collaborate to support the firm’s global footprint of investment strategies and teams.

What you’ll do  

• Design, develop, and maintain robust data pipelines and ETL workflows in Databricks to support quantitative research, trading, and risk management. 


• Collaborate closely with data scientists, analysts, and portfolio managers to understand data needs and deliver scalable data infrastructure. 


• Ingest, process, and normalize large volumes of structured and unstructured financial data from a variety of sources. 


• Optimize performance of data pipelines and ensure high availability, reliability, and data quality across all production systems. 


• Implement data governance best practices, including data lineage, cataloging, auditing, and access controls. 


• Support the integration of third-party data vendors and APIs into the broader data ecosystem. 


• Continuously evaluate and implement new tools and technologies to improve data engineering capabilities, with a focus on cloud-native and distributed processing frameworks.

  What’s required  

• 3–6 years of professional experience in data engineering or a similar role, ideally within a financial services or high-performance computing environment.


• Expertise in Databricks, including Spark (PySpark or Scala), Delta Lake, and notebook-based development workflows.


• Proficiency in building scalable, distributed data pipelines in a cloud environment (preferably Azure or AWS).


• Strong programming skills in Python and SQL.


• Solid understanding of data architecture principles, data modeling, and data warehousing.


• Experience with version control (e.g., Git), CI/CD workflows, and modern data orchestration tools (e.g., Airflow, dbt).


• Demonstrated ability to work collaboratively in a fast-paced, high-stakes environment with both technical and non-technical stakeholders.


• Bachelor’s or master’s degree in computer science, engineering, or a related technical field.


• Commitment to the highest ethical standards

WE TAKE CARE OF OUR PEOPLE

• Fully paid health care benefits


• Generous parental and family leave policies


• Mental and physical wellness programs


• Volunteer opportunities


• Non-profit matching gift program


• Support for employee-led affinity groups representing women, minorities and the LGBTQ+ community


• Tuition assistance


• A 401(k) savings program with an employer match and more

About Point72

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth. For more information, visit  .

The annual base salary range for this role is $180,000-$225,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

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