About the role
from listingBuild the cloud data platform that powers enterprise analytics — hands-on AWS, Terraform, and Databricks engineering at scale
Your expertise in cloud infrastructure and data platforms can shape how an entire enterprise accesses, processes, and trusts its data. At JPMorganChase, we build platforms that operate at a scale few organizations ever encounter — and we do it with a team that values craftsmanship, collaboration, and continuous improvement. This is an opportunity to grow your skills, deepen your cloud and data platform expertise, and make a direct impact on solutions that matter to the business and the people it serves. As a Software Engineer III at JPMorganChase within the Cloud Data Platform team, you will be hands-on designing, provisioning, and operating a modern cloud data platform built on AWS and Databricks. You will partner with data engineers, security, and platform teams to deliver infrastructure that is scalable, secure, and production-ready. Your work will enable reliable, well-governed analytics workloads that drive insight and decision-making across the firm. Job responsibilities Design, build, and maintain cloud infrastructure on AWS using Terraform, including networking, security, compute, storage, and managed services, following infrastructure-as-code best practices Develop and manage Databricks platform capabilities — including workspaces, clusters, policies, jobs, libraries, and integrations — to support production-grade data workloads Build automation around platform provisioning and operations using Python or Java, reducing manual effort and improving consistency across environments Create and maintain CI/CD pipelines for infrastructure and platform deployments, including Terraform plan/apply workflows, policy checks, and automated testing Implement security best practices across the platform, including least-privilege access controls, secrets management, encryption, network segmentation, and auditability Improve platform reliability through observability tooling — logging, metrics, and tracing — alongside alerting, incident response practices, and performance and cost optimization Collaborate with stakeholders to translate business and technical requirements into scalable platform designs, and document standards, patterns, and runbooks for team-wide use Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards Apply 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 Formal training or certification on software engineering concepts and proficient applied experience Strong hands-on experience provisioning AWS infrastructure with Terraform, including modules, remote state, workspace/environment management, and change management workflows Solid experience with core AWS services relevant to cloud data platforms, such as VPC, IAM, S3, EC2/ECS/EKS, RDS, KMS, CloudWatch, and load balancing Working experience with Databricks, including platform administration and/or building and supporting production workloads Proficiency in Python (preferred) or Java, with demonstrated ability to build automation, tooling, and platform integrations Experience with Git-based workflows and CI/CD practices applied to infrastructure and platform delivery Strong troubleshooting skills across cloud infrastructure, distributed systems, and data or platform pipelines Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations Preferred qualifications, capabilities, and skills Experience with data lakehouse architectures using Delta Lake, Apache Spark, or similar open-source frameworks Familiarity with AWS-native data services such as Glue, Lake Formation, Redshift, or Athena Exposure to data governance practices including cataloging, lineage tracking, and data access control at the platform layer AWS certification(s) in cloud architecture, data, security, or DevOps disciplines Experience working in regulated industries where security, auditability, and compliance are core platform requirements