Lead Software Engineer- Python / Big Data / Aws/ Etl Pipelines

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Corporate Sector

Lead Software Engineer focused on Python, Big Data, AWS, and ETL pipelines within a fintech environment. The role involves designing, developing, and optimizing data pipelines, analytics workflows, and data models, with a strong emphasis on leveraging AI, machine learning, and advanced analytics for risk insights and anomaly detection. The engineer will also champion cutting-edge technologies and ensure data quality, security, and governance.

What you'd actually do

  1. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  2. Develops secure high-quality production code, and reviews and debugs code written by others
  3. Defines and drives the technology roadmap, focusing on next-generation data, analytics, and automation platforms
  4. Leads the adoption and integration of cutting-edge technologies including Databricks, Snowflake, AWS, and modern web frameworks
  5. Architects scalable, secure, and resilient solutions for valuation control, pricing, and regulatory compliance

Skills

Required

  • Python
  • Databricks
  • Spark
  • data pipeline development
  • AWS
  • Snowflake
  • Agile development
  • code reviews
  • automated testing
  • CI/CD pipelines
  • documentation
  • data cataloging
  • metadata management
  • data discovery tools
  • automation
  • continuous delivery
  • Software Development Life Cycle
  • CI/CD
  • Application Resiliency
  • Security

Nice to have

  • AI / BI Tools
  • modern web development frameworks
  • React
  • Angular
  • AI
  • machine learning
  • advanced analytics techniques
  • implementing ML models in production
  • financial markets
  • risk management
  • valuation control functions
  • vendor-based financial software solutions
  • integration

What the JD emphasized

  • regulatory compliance
  • AI, machine learning, and advanced analytics

Other signals

  • Champions the use of AI, machine learning, and advanced analytics to deliver real-time risk insights and anomaly detection
  • Designs and implements data pipelines, analytics workflows, and reporting solutions using Python, Databricks, and Spark
  • Builds and optimizes data models, ETL/ELT processes, and integration frameworks for large-scale financial data
  • Ensures data quality, security, lineage, and governance across all team platforms