Lead Software Engineer [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Asset & Wealth Management

Lead Software Engineer at JPMorgan Chase focused on designing, implementing, and validating data pipelines, reporting solutions, and portfolio optimization processes for systematic portfolio management in financial services. The role involves leading teams, collaborating with stakeholders, overseeing cloud infrastructure, and developing financial analytics and back testing frameworks. Requires experience with optimization solvers, financial data analysis, Python programming, cloud platforms, and CI/CD tools.

What you'd actually do

  1. Lead the design, implementation, and validation of data pipelines, reporting solutions, and portfolio optimization processes to enhance systematic portfolio management.
  2. Drive cross-functional collaboration between business and technology stakeholders, define requirements, set priorities, and ensure delivery of high-quality solutions.
  3. Mentor team members, oversee best practices, and foster a culture of innovation and continuous improvement.
  4. Ensure alignment with organizational goals and regulatory standards while managing resources and project timelines.
  5. Oversee private and public cloud computing infrastructure, version control, software releases, and the development of reusable software development kits (SDKs) to improve software maintainability, scalability, and minimize operational risk.

Skills

Required

  • Designing and developing software solutions to support systematic portfolio management and optimization in financial services environments employing optimization solvers including Gurobi, MSCI Open Optimizer and Axioma, technologies including React, Java, JavaScript, Python, Relational, NoSQL and Object-Oriented Databases
  • Analyzing Bloomberg, FactSet, MSCI and internally-sourced quantitative financial data to generate performance metrics and translate results into actionable insights for both technical and non-technical stakeholders
  • Collaborating with quantitative researchers and portfolio managers to implement analytics, modeling frameworks, and investment strategy tools including asset correlation and covariance matrices, risk factor exposure, mean- variance and performance attribution analysis
  • Developing financial applications using advanced Python programming, leveraging numerical and data analysis libraries including NumPy, pandas, and optimization libraries, Gurobi
  • Building and maintaining RESTful APIs using Python web frameworks including Flask and FastAPI to support integration with investment platforms
  • Using Jupyter Notebook for prototyping, visualization, and exploratory analysis of financial data and models
  • Architecting and managing cloud-native solutions, including serverless computing with platforms AWS Lambda, Amazon SQS, and Microsoft Azure to support scalable application deployment
  • Modernizing and automating ETL (Extract, Transform, Load) processes using tools including Apache Airflow for workflow orchestration and Docker for containerization and environment consistency
  • Optimizing financial data querying and persistence across Apache Cassandra, Amazon S3, and MSSQL databases
  • Improving code quality and reliability with software engineering practices of Test and Behavior Driven Development using behave and pytest-bdd
  • Conducting unit testing using unittest and integration testing using pytest
  • Utilizing parallel and asynchronous programming using asyncio and multiprocessing to speed up grid search hyperparameter tuning for optimization strategy back testing
  • Conducting version releases using continuous integration and continuous delivery tools including Jenkins and Spinnaker

What the JD emphasized

  • systematic portfolio management
  • optimization solvers
  • financial services environments
  • quantitative financial data
  • quantitative researchers
  • financial model validation
  • financial data
  • cloud-native solutions
  • workflow orchestration
  • containerization
  • software engineering practices
  • hyperparameter tuning
  • continuous integration and continuous delivery