We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan Chase within the Corporate and Investment Banking (CIB) Payments Technology team, you play a crucial role in an agile team that is dedicated to improving, creating, and delivering trusted market-leading technology products in a secure, stable, and scalable manner. As a key technical contributor, you are tasked with implementing vital technology solutions across numerous technical areas within various business functions to support the firm's business goals.
As a Regulatory Reporting Lead engineer, you will be accountable for end-to-end technical delivery across Regulatory initiatives. You will also help shape how the team applies modern data platforms (Databricks, Spark) and AI-assisted engineering to reporting workloads.
Job responsibilities
- 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
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Build and optimize large-scale data and reporting pipelines on Databricks/Spark, including data quality, lineage, and reconciliation controls for regulatory submissions
- Applies AI and machine-learning techniques and AI-assisted engineering tools to accelerate delivery and automate reporting and remediation workflows
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Closely work with external vendors, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required qualifications, capabilities, and skills
- Bachelor’s Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
- Formal training or certification on engineering concepts and 5+ years of experience in system engineering or software development.
- Hands-on practical experience delivering system design, application development, testing, and operational stability.
- Proficient in coding using the following technology stack: Java, Junit, Maven, Hibernate, Spring Boot, Spring JPA, Spring Batch
- Hands-on experience with Databricks and/or Apache Spark for batch data processing — Delta Lake, Spark SQL, PySpark or Spark with Java/Scala, and job orchestration
- Strong programming experience in Python for data engineering, automation, and scripting
- Familiarity with one or more DBMS like Oracle, MySQL, or others
- Proficient in all aspects of the Software Development Life Cycle
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Advanced understanding of agile methodologies such as CI/CD, Applicant Resiliency, and Security and proficient in automation and continuous delivery methods
Preferred qualifications, capabilities, and skills
- Previous working experience with Payment technology, and/or Reg Reporting tech will be a plus
- In-depth knowledge of the financial services industry (payment products preferred) and related regulatory landscape and their IT systems
- Experience applying AI/ML models or large language models (LLMs) to data pipelines, reconciliation, or anomaly detection
- Exposure to cloud technologies, Splunk, Apache Kafka, Grafana