Lead Software Engineer-Python/Data Engineering

JPMorgan Chase & Co.•Wilmington, DE

About The Position

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-Python/Data Engineering, Machine Learning and Cloud at JPMorgan Chase within the Corporate Technology- Consumer & Community Bank Finance group, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor and lead, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability.
Demonstrates a proactive attitude and eagerness to learn emerging technologies, including AI and ML.
Experience in technologies such as Pyspark, Kafka, Terraform, Kubernetes.
Experience with AWS services, including but not limited to ECS, EMR, Lambda, EC2, TensorFlow and SageMaker
Experience in programming languages such as Python or Java.
Understanding of and ability to learn the basic architecture of Cloud services and usage.
Experience working with databases such as Oracle or Cassandra.
Exposure to Agile and scrum methodologies.
Familiarity with CI/CD, Application Resiliency, and Security.

Nice To Haves

Background with Machine Learning Frameworks and MLOps.
Python Machine Learning library and ecosystem experience (Pandas and Numpy etc.)
Experience or knowledge in Databricks.

Responsibilities

Executes standard software solutions, design, development, and technical troubleshooting
Provides Technical Leadership, guidance and direction to other team members
Writes secure and high-quality code using Python or Java programming languages
Establish standards, guidance and best practices for the technical aspects in the team
Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation
Applying technical troubleshooting skills to analyze and resolve technical issues
Leverages Cloud services to build ML pipeline to implement, deploy and productionize ML Models
Works with Business stakeholders and Product Owners to understand requirements.
Handle MLOps tasks and any associated infrastructure or production changes in coordination with SRE.