Vice President; Software Engineer

Bank of America Bank of America · Banking · Jersey City, NJ

Software Engineer (VP) at Bank of America focused on building and deploying machine learning pipelines for model training and inference, integrating predictions into dashboards, and developing data processing and ETL pipelines. Requires experience with Spark, Hadoop, Trino, Airflow, Python, AWS, and CI/CD.

What you'd actually do

  1. Design and develop complex requirements to accomplish business goals.
  2. Ensure that software is developed to meet functional, non-functional, and compliance requirements.
  3. Ensure solutions are well designed with maintainability/ease of integration and testing built-in from the outset.
  4. Contribute to story refinement/defining requirements.
  5. Participate in estimating work necessary to realize a story/requirement through the delivery lifecycle.

Skills

Required

  • Apache Spark
  • Hadoop
  • Trino
  • Apache Airflow
  • Python
  • AWS (S3, Lambda)
  • Tableau
  • CI/CD
  • DevOps
  • SQL
  • ETL
  • distributed data processing
  • machine learning pipelines
  • model training
  • model inference
  • real-time analytics
  • business intelligence

What the JD emphasized

  • Developing distributed data processing pipelines using Apache Spark and Hadoop to support both batch and real-time analytics workloads
  • Writing complex SQL queries using Trino to enable high-performance federated querying across multiple heterogeneous data sources
  • Building scalable ETL pipelines using Apache Airflow and Python to process structured and unstructured financial and operational datasets
  • Designing and deploying machine learning pipelines using Python and AWS (S3, Lambda) for model training and interference
  • Integrating AI/ML-driven predictions into Tableau dashboards using Python scripting for real-time analytics and business intelligence
  • Implementing CI/CD pipelines, automating test suites (integration, regression, performance), and ensuring reliable DevOps delivery

Other signals

  • designing and deploying machine learning pipelines using Python and AWS (S3, Lambda) for model training and interference
  • integrating AI/ML-driven predictions into Tableau dashboards using Python scripting for real-time analytics and business intelligence