If you are a software engineering leader ready to take the reins and drive impact, we’ve got an opportunity just for you.
As a Director of Software Engineering – Payment Data Platform Infrastructure at JPMorgan Chase within the Commercial and Investment Banking – Data Analytics Payment Team, you own the infrastructure, reliability, and security posture underpinning the BRIE data platform and the NEO agent runtime. You set technical direction across infrastructure engineering, site reliability, and security operations, and you are accountable for these mission-critical systems running securely, stably, and at scale across a multi-region AWS and on-premises estate where the stakes and regulatory bar are high.
Job responsibilities
- Provides overall direction, oversight, and coaching for a team of engineering managers and senior technologists spanning infrastructure engineering, SRE, and SecOps for both BRIE and NEO
- Owns the reliability strategy for the platform estate — SLOs, error budgets, capacity planning, and disaster recovery — sustaining active-active, multi-region operation across AWS and on-prem while meeting the platform’s high-availability commitments
- Sets the security operations agenda: threat detection and response, vulnerability and patch management, secrets and key management, and evidence for audit, risk, and regulatory reviews, maintaining CPOF compliance across all environments
- Champions infrastructure-as-code, immutable deployments, and platform automation so provisioning, scaling, and remediation are repeatable, reviewable, and auditable
- Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
- 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 and support capacity unlock initiatives at scale.
- Leads incident command for major events, drives blameless postmortems, and closes the loop on systemic remediation to raise overall operational stability
- Anticipates the infrastructure needs of the data platform and agent runtime — compute, storage, networking, and GPU serving/training capacity — and translates them into a funded, prioritized roadmap
- Makes decisions that influence resourcing, budget, tooling, and vendor selection across the infrastructure organization, and is accountable for those outcomes
- Leads evaluation sessions with cloud providers, vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability within existing systems and information architecture
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Partners with platform, product, data, and InfoSec stakeholders, and communicates reliability, security, and cost trade-offs to senior leadership
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 8+ years applied experience, including significant time leading infrastructure, SRE, or platform organizations, with experience managing managers
- Experience leading teams of technologists and managing budget, resourcing, and delivery across multiple concurrent workstreams
- Hands-on background in large-scale distributed systems: system design, application development, testing, and operational stability
- Deep expertise operating production systems across public cloud and on-premises data centers, including multi-region, active-active resilience and disaster recovery
- Demonstrated ownership of a security posture in a regulated environment — SecOps, identity and access, secrets management, audit, and regulatory compliance
- Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Advanced in one or more programming language(s), with the ability to guide critical technical decisions
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Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, distributed data systems)
Preferred qualifications, capabilities, and skills
- Deep AWS experience across multi-account, multi-region architectures, paired with on-premises data center operations
- Expert-level Kubernetes and containerized platform operations, including multi-tenant isolation (siloed and pooled) and noisy-neighbor controls
- Proficiency with Infrastructure as Code (Terraform) and GitOps-driven, immutable deployment pipelines
- Experience operating policy and authorization at the infrastructure layer (e.g., OPA/Rego, OpenFGA) and policy-as-code workflows and experience operating Databricks (Spark) and real-time stream processing with Apache Flink at production scale
- Strong observability and SRE tooling background (e.g., Splunk, OpenTelemetry, Prometheus/Grafana) with SLO/error-budget practice
- Experience running GPU compute fleets for ML serving and training (e.g., NVIDIA L40S/H100, SageMaker) and associated capacity and cost governance and experience with regulated-environment compliance frameworks and evidencing controls to risk, audit, and InfoSec
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Familiarity building and running Java Spring Boot GraphQL services for high-availability, low-latency APIs and familiarity with the data platform stack — lakehouse and open table formats (Apache Iceberg), streaming and batch pipelines, and OLAP/columnar stores