What are the responsibilities and job description for the Manager of Software Engineering: Data Analytics position at JPMorgan Chase?
Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.
As a Manager of Software Engineering: Data Analytics at JPMorganChase within the CONSUMER & COMMUNITY BANKING, Connected Commerce - Banking Payments, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Provides guidance to immediate team of software engineers on daily tasks and activities
- Sets the overall guidance and expectations for team output, practices, and collaboration
- Anticipates dependencies with other teams to deliver products and applications in line with business requirements
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and data pipelines
- Manages stakeholder relationships and the team’s work in accordance with compliance standards, service level agreements, and business requirements
Required qualifications, capabilities, and skills
- Formal training in software engineering concepts and 5 years of applied experience
- Extensive experience building and operating AWS/public cloud–based applications
- Strong Python (Pyspark) programming skills
- Proven hands‑on delivery across system design, application development, testing, and operational stability.
- Experience managing technologists
- Proficient in automation and continuous delivery methods
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Experience with pipelines and DAGs for data processing and/or machine learning
- Demonstrated proficiency in cloud and AI/ML software practices
Experience driving adoption of AI engineering tools (e.g., GitHub Copilot) for JIRA, documentation, coding, and releases, with measurable productivity and quality gains
- AWS (hands-on): Glue, EventBridge, Step Functions, S3, Lambda, ECS, EKS, Kinesis, CloudWatch
- Outside AWS: Python, Terraform, TigerGraph, graph databases, GitHub Copilot, Airflow, Kubernetes
- JPMC platforms/tools (highly preferred): Jules/JET, GKP (Gaia Kubernetes) – For Internal to JPMC