JPMorgan Chase AM- Data Engineer- Machine Learning Workloads in New York, New York

The candidate will be responsible for:

  • Design, construct, install, test and maintain highly scalable data management systems

  • Bring structure to large, disparate sources of data ranging from highly structured market data through to fully unstructured text

  • Build high-performance algorithms, prototypes, predictive models and proof of concepts

  • Design and structure a research environment flexible enough for creative research whilst stable enough to generate investment ideas

  • Integrate new data management technologies and software engineering tools into existing structures

  • Recommend ways to improve data reliability, efficiency and quality

  • Collaborate with data analysts and modelers on project goals

Asset Management Technology Equities is seeking a well-rounded hands-on Data Engineer that is experienced in building data pipelines and running analytics workbenches for data exploration and machine-learning workloads. The candidate will be part of the Global Equities Data Science team, collaborating with data scientists on the design and implementation of predictive models. The candidate should have strong data engineering experience of managing data, integrating with third-party APIs, coding Python modules, and managing cloud environments.

J.P. Morgan Asset & Wealth Management, with client assets of $2.4 trillion, is a global leader in investment and wealth management. Its clients include institutions, high-net-worth individuals and retail investors in every major market throughout the world. The division offers investment management across all major asset classes including equities, fixed income, alternatives, multi-asset and money market funds. For individual investors, the business also provides retirement products and services, brokerage and banking services including trusts and estates, loans, mortgages and deposits.


  • Python

  • Database architectures and storage structures (Parquet)

  • Hadoop-based technologies (Spark, HDFS)

  • Analytics workbenches (Jupyter, AWS Sagemaker, Domino Data Lab)

  • Data mining, statistical analysis and machine learning (Pandas, Keras)

  • Docker containers

  • AWS and Google Cloud

Business Knowledge and others:

  • Creative Problem-Solving: Approaching data organization challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.

  • Effective Collaboration: Carefully listening to management, data scientists and data architects to establish their needs.

  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve data management problems.

  • Industry Knowledge: Understanding the equities markets and how data can be collected, analyzed and utilized; maintaining flexibility in the face of big data developments.

JPMorgan Chase is an equal opportunity and affirmative action employer Disability/Veteran.