Lead AWS Software Engineer - Big Data ETL Excellence

JPMorgan Chase & Co.

Fairfax (DE)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Job summary

JPMorgan Chase & Co. is seeking an AWS Lead Software Engineer specializing in Big Data/ETL to join the Model Delivery and Platform Engineering team.

The role focuses on delivering trusted, scalable technology products with a strong emphasis on reliability, security, and operational excellence. You will drive automation, enforce risk controls, and oversee AI-assisted development practices across multiple business functions, collaborating with product and engineering teams to achieve enterprise

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Proven track record designing, coding, testing, and delivering production-grade software in at least one technology stack.
Advanced development experience in Java or Python, with debugging/troubleshooting skills for complex production issues.
Hands-on experience with AWS/other clouds and container platforms/orchestration (e.g., Docker, Kubernetes, ECS).
Experience building scalable data processing and Big Data/ETL pipelines (e.g., Hortonworks, AWS-based solutions).
Working knowledge of core infrastructure (routers, load balancers, compute, storage, networks, cloud products) and ability to troubleshoot networking technologies.
Deep proficiency in reliability, scalability, performance, security, enterprise architecture, and toil reduction; able to implement SRE practices in apps/platforms.
Proficiency in monitoring/telemetry tools (Grafana, Dynatrace, Prometheus, Datadog, Splunk).
Learn/evaluate new tech, teach languages to others, and collaborate across levels and stakeholder groups.
Strong foundations in software applications/processes with depth in a discipline; ability to solve complex data structures/algorithms problems; experience with AI-assisted software development tools.

Responsibilities

Designs, codes, tests, and delivers automation to streamline AO workstreams (including LLMs/agents).
Governs application risk, controls, and compliance; partner with Technology Risk & Controls and drive remediation.
Owns security and data accountability for the application; ensure strong authentication/authorization and proper data handling.
Coordinates across product and engineering to prioritize and drive execution for AO workstreams.
Runs resilient, well-operated production services end-to-end with monitoring and incident management.
Champions site reliability culture and practices across the team; lead initiatives to improve reliability and service levels.
Collaborates to establish service level indicators and error budgets with customers.
Documents and shares knowledge within the organization via internal forums and communities of practice.
Drives adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Applies SDLC tools and AI-assisted development capabilities to improve automation value.