Data Engineer III

JPMorgan Chase & Co.•Columbus, OH

About The Position

You’ll join a team where your work directly improves how we build, deploy, and run data products at scale. You’ll collaborate closely with engineers and data scientists, apply modern cloud and automation practices, and help deliver solutions that are reliable, secure, and designed for long-term use. As a Data Engineer at JPMorganChase within the Consumer & Community Banking – Authentication Tech team, you will be an integral part of an agile team that enhances, builds, and delivers trusted, market-leading technology products in a secure, stable, and scalable way. You’ll drive business impact through strong engineering practices, deep technical expertise, and a disciplined approach to solving complex problems across data, cloud infrastructure, and machine learning operations.

Requirements

Formal training or certification on software engineering concepts and 3 + applied experience.
5+ years of hands-on experience in data engineering or a closely related engineering role.
Hands-on practical experience delivering system design, data pipeline development, testing, and operational stability.
Proficiency with Amazon Web Services (for example Amazon Elastic Kubernetes Service, Amazon EC2, Amazon S3, and AWS Lambda).
Proficiency automating infrastructure using Terraform.
Working knowledge of MLOps practices, large-scale data processing, and cloud-native architectures.
Demonstrated ability to independently solve complex design and functionality problems while partnering effectively with stakeholders.

Nice To Haves

Experience applying generative AI or machine learning techniques, including transformer-based concepts such as tokenization and embeddings.
Experience in financial services or other highly regulated industries.
Familiarity with data security, privacy, and compliance requirements in cloud environments.

Responsibilities

Design, build, and maintain scalable data pipelines and extract, transform, load (ETL) processes using Amazon Web Services.
Develop and support cloud-native data processing solutions with a focus on performance, reliability, and operational excellence.
Automate infrastructure provisioning and lifecycle management using Terraform.
Build and maintain machine learning operations (MLOps) capabilities for model training, deployment, monitoring, and governance.
Engineer solutions to handle large datasets with appropriate resiliency, observability, and cost awareness.
Partner with software engineers and data scientists to deliver end-to-end data and AI solutions from development through production.
Contribute to integrating generative AI and machine learning patterns, including transformer-based approaches (for example tokenization and embeddings), where applicable to business use cases.
Participate in agile ceremonies, code reviews, and design discussions to continuously improve engineering quality.