AWS Lead Software Engineer - Big Data/ETL

Salary: $61,000 - 101,000 per year Requirements: Formal training or certification in software engineering concepts, with at least 5 years of applied experience. Proven ability to design, code, test, and deliver production-grade software in at least one technology stack. Advanced Java or Python development experience and strong debugging and troubleshooting skills for complex production issues. Hands-on experience with AWS or other cloud platforms and container technologies or orchestration tools such as Docker, Kubernetes, or ECS. Experience developing scalable data-processing and Big Data/ETL pipelines, using technologies such as Hortonworks or AWS-based solutions. Working knowledge of infrastructure including routers, load balancers, compute, storage, networks, and cloud products, with the ability to troubleshoot common networking issues. Deep knowledge of reliability, scalability, performance, security, enterprise architecture, and toil reduction, with the ability to apply SRE practices to applications and platforms. Proficiency in white-box and black-box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, or Splunk. Ability to evaluate new technologies, teach programming languages to others, and collaborate with stakeholders at different levels. Strong software application and process fundamentals, emerging depth in a technical discipline, and the ability to solve complex data-structure and algorithm problems. Demonstrated experience leading the effective use of approved AI-assisted software development tools for tasks such as coding, code review, test acceleration, and troubleshooting; ability to set expectations for validating AI outputs for correctness, performance, and security. Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and security and resiliency expectations; experience coaching engineers in safe, compliant adoption. Preferred: Current AWS credential, such as Cloud Practitioner, Solutions Architect, SysOps Administrator, or Developer, demonstrating knowledge of core AWS services, cloud architecture, IAM, VPC networking, monitoring and logging, and cost awareness. Preferred: Recognized automation or DevOps credential in areas such as Kubernetes or containers, CI/CD, Infrastructure as Code, or site reliability. Responsibilities: Design, code, test, and deliver automation, including LLMs and agents, to reduce manual operational work and streamline AO workstream remediation, including control items, security vulnerabilities, upgrades, and FARM findings. Govern application risk, controls, and compliance by upholding firm standards, partnering with Technology Risk & Controls, managing Technology Lifecycle Management, and driving issues and findings to closure through remediation and evidence management. Own application security and data accountability, ensuring strong authentication and authorization, vulnerability and certificate hygiene, accurate data registration and classification, and compliant storage, retention, and disposal. Coordinate product and engineering teams to prioritize and deliver AO workstreams with urgency, influencing and coaching teams and aligning execution across large developer communities. Operate production services end to end by implementing monitoring, logging, and anomaly detection; maintaining secure network configurations and least privilege; and leading or supporting incident, problem, change, recovery, and resiliency activities. Champion site reliability practices and provide technical influence across the team. Lead data-driven initiatives to improve the reliability and stability of team applications and platforms and raise service levels. Work with team members to define service level indicators and with stakeholders to establish appropriate service level objectives and error budgets. Document and share knowledge through internal forums and communities of practice. Promote enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational results, including code review, refactoring, test strategy, and incident analysis; establish validation standards for secure coding, peer review, and automated testing, and encourage reuse of effective approaches. Apply software development lifecycle tools, including enterprise-authorized AI-assisted development and automation capabilities, to increase the value of automation. Technologies: AI AWS Architect Big Data CI/CD Cloud Datadog DevOps Docker Dynatrace ETL Grafana IAM Support Java Kubernetes Marketing Network Prometheus Python Security Splunk More: We are hiring an AWS Lead Software Engineer in Big Data/ETL for JPMorgan Chases Corporate Sector Model Delivery and Platform Engineering team. You will contribute as a core technical member of an agile team building and enhancing secure, stable, scalable technology products in support of the firms business objectives. We offer a competitive total rewards package, with base pay determined by role, experience, skills, and location; eligible roles may also include commission-based pay or discretionary incentives. Benefits, subject to eligibility, include health care coverage, on-site health and wellness centers, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. Our corporate functions span areas including finance, risk, human resources, and marketing, supporting our businesses, clients, customers, and employees. We value diversity and inclusion, are an equal opportunity employer, and provide reasonable accommodations for religious practices and beliefs and for mental or physical disability needs. J.P. Morgan Chase & Co. serves consumers, small businesses, and corporate, institutional, and government clients, with businesses spanning investment banking, consumer and small business banking, commercial banking, financial transaction processing, and asset management. last updated 42 week of 2026