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$174,600–$270,981.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
on FindRole.
Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Director of Software Engineering - Payment Data Platform Infrastructure within the Commercial and Investment Banking - Data Analytics Payment Team, you will lead a team of engineering managers and technologists across infrastructure, SRE, and SecOps. You are responsible for the reliability, security, and scalability of the BRIE data platform and NEO agent runtime across multi-region AWS and on-premises environments. Your daily work involves managing site reliability strategies like SLOs and disaster recovery, overseeing security operations in regulated environments, and championing infrastructure-as-code and immutable deployments. You will drive agentic AI-enabled engineering practices and manage the roadmap for compute, storage, and GPU serving capacity. Required skills include expertise in distributed systems, Kubernetes, Terraform, GitOps, and Spark. You will also navigate complex technical trade-offs regarding security, cost, and performance for high-availability data infrastructure.
What does a Software Engineering Director earn?
Median $262275 from 30 postings across 13 companies.
Manage a team of engineering managers and senior technologists across infrastructure, SRE, and security operations.
Own the reliability strategy including SLOs, capacity planning, and disaster recovery for multi-region AWS and on-premises environments.
Establish the security operations agenda for threat detection, vulnerability management, and regulatory compliance.
Drive infrastructure-as-code, immutable deployments, and platform automation to ensure scalable and auditable provisioning.
Lead the integration of agentic AI-enabled engineering practices and SDLC automation to improve delivery speed and quality.
Manage incident command for major events and lead blameless postmortems to drive systemic remediation.
Develop a funded roadmap for infrastructure needs including compute, storage, networking, and GPU serving capacity.
Make critical decisions regarding budget, tooling, vendor selection, and resource allocation across the organization.
What we're looking for
Formal training or certification in software engineering concepts.
8+ years of experience including significant time leading infrastructure, SRE, or platform organizations with experience managing managers.
Experience leading teams of technologists while managing budget, resourcing, and delivery across multiple concurrent workstreams.
Hands-on background in large-scale distributed systems, including system design, application development, testing, and operational stability.
Deep expertise operating production systems across public cloud and on-premises data centers with multi-region, active-active resilience and disaster recovery.
Demonstrated ownership of a security posture in a regulated environment, including SecOps, identity management, and audit compliance.
Experience leading adoption of agentic AI-enabled engineering practices and understanding of responsible AI use in engineering workflows.
Advanced proficiency in at least one programming language and advanced understanding of CI/CD, application resiliency, and security.
Deep AWS experience across multi-account, multi-region architectures with on-premises data center operations (preferred).
Expert-level Kubernetes and containerized platform operations including multi-tenant isolation (preferred).
Proficiency with Infrastructure as Code (Terraform) and GitOps-driven deployment pipelines (preferred).
Experience operating policy at the infrastructure layer, Databricks, and Apache Flink at production scale (preferred).
Strong observability and SRE tooling background with SLO/error-budget practice (preferred).
Experience running GPU compute fleets for ML serving and training in regulated environments (preferred).
Familiarity building Java Spring Boot GraphQL services and experience with the data platform stack (preferred).