Data Engineer, Technology
Point72 Asset Management - New York | Stamford
Posted Jun 14, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Data Engineer, Technology New York | Stamford 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. -
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