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As an AWS Lead Software Engineer-Big Data/ETL at JPMorgan Chase within the Corporate Sector's Model Delivery and Platform Engineering team, 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, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Designs, codes, tests, and delivers automation (including LLMs/agents) to eliminate manual operational work and streamline AO workstreams’ remediations (e.g., control items, security vulnerabilities, upgrades, and FARM findings)
- Governs application risk, controls, and compliance: own adherence to firm standards, partner with Technology Risk & Controls, manage Technology Lifecyle Management (TLM), and drive closure of issues/findings (e.g., FARM) through effective remediation and evidence management
- Owns security and data accountability for the application: ensure strong authentication/authorization, vulnerability and certificate hygiene, and proper data registration/classification plus compliant storage/retention/disposal
- Coordinates across product and engineering at scale to prioritize and drive execution with a clear sense of urgency for AO workstreams (including influencing/coaching teams and aligning execution across large developer communities)
- Runs resilient, well-operated production services end-to-end: implement monitoring/logging and anomaly detection, maintain secure network configurations/least privilege, and lead/support incident/problem/change management and recovery/resiliency readiness
- Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team
- Leads initiatives to improve the reliability and stability of your team’s applications and platforms using data-driven analytics to improve service levels
- Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers
- Documents and shares knowledge within your organization via internal forums and communities of practice
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Applies 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 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 excellent 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 common networking technologies/issues
- Deep proficiency in reliability, scalability, performance, security, enterprise architecture, and toil reduction, with ability to implement SRE practices in apps/platforms
- Proficiency in white/black box monitoring, SLO alerting, telemetry collection, using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
- Learn/evaluate new tech, teach languages to others, and collaborate across levels and stakeholder groups.
- Strong foundations in software applications/processes (with emerging depth in a discipline), ability to solve complex data structures/algorithms problems, drive to self-Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
- Current AWS credential (e.g., Cloud Practitioner, Solutions Architect, SysOps Administrator, or Developer) demonstrating working knowledge of core AWS services, cloud architecture patterns, security and identity fundamentals (IAM), networking basics (VPC), monitoring/logging, and cost-awareness best practices.
- Recognized automation/DevOps credential (e.g., Kubernetes/containers, CI/CD, Infrastructure as Code, or site reliability)