When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.

As a Senior Manager of Software Engineering at JPMorgan Chase within the Corporate Technology, you serve in a leadership role by providing technical coaching and hands on support for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Job responsibilities

• Provide overall direction, oversight, and coaching for a team of entry-level to mid-level software engineers that work on basic to moderately complex tasks
• Be accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures
• Communicate status, risks, and decisions crisply to senior stakeholders; translate business needs into technical outcomes and measurable KPIs/OKRs
• Actively participate in defining and reviewing solution architecture, reference architectures, and runway/tech-debt remediation plans.
• Define, measure, and govern NFRs (reliability, performance, scalability, security, compliance, operability, cost efficiency); ensure they are first-class acceptance criteria.
• Champion observability (SLIs/SLOs, error budgets, tracing, logging) and resilience engineering (chaos testing, fault injection, graceful degradation).
• Provides input to leadership regarding budget, approach, and technical considerations to improve operational efficiencies and functionality for the team
• Optimize cost and performance for cloud/on-prem workloads; advocate sensible SRE practices and automation to reduce toil.
• Creates a culture of diversity, opportunity, inclusion, and respect for team members and prioritizes diverse representation
• Uphold firm code of conduct and ethics; ensure the team adheres to engineering controls, data handling, and information security policies.
• Encourage innovation and experimentation, pilot new tools/practices responsibly, and scale what works.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
• Strong coaching, performance management, and conflict resolution skills, Ability to guide and coach teams on approach to achieve goals aligned against a set of strategic initiatives such as Reliability, Resiliency and Cost Optimization
• Hands-on experience designing and reviewing distributed systems and APIs; solid understanding of microservices, event-driven architectures, data modeling, and integration patterns.
• Proficiency in at least one modern language and ecosystem (e.g., Java, C#, Python) and common frameworks.
• Strong grasp of NFRs and how to instrument and test them: load/perf testing, capacity planning, chaos/failure testing, security controls.
• Experience with at least one major cloud (AWS), containerization (Docker/Kubernetes), and infrastructure-as-code (Terraform/CloudFormation).
• CI/CD expertise (e.g., Jenkins/GitHub Actions/Azure DevOps), artifact management, automated testing, and progressive delivery.
• Observability stack (Prometheus/Grafana, OpenTelemetry, ELK, Splunk, Datadog, or similar); incident management and on-call best practices.
• Experience with hiring, developing, and retaining talent

• Ability to use AI Tool and techniques for building reliable, secure and cost-effective IT solutions, e.g. CoPilot, LLM etc.

• Excellent written and verbal communication; able to present trade-offs and drive consensus across business, product, and technology.

Preferred qualifications, capabilities, and skills

• Design and develop Observability & Telemetry strategy for both Batch and real-time complex IT applications in line with Business SLAs

• Knowledge of credit products and structures, e.g. cards, personal loans, auto, mortgages, revolving facilities, term loans

• Familiarity with decision engines and data: bureau data, alternative data, internal behavioral data, reason codes/adverse action, and model monitoring

• Strong partnership with Risk, Product, Compliance, Fraud, and Data Science to align strategy, controls, and implementation timelines.

• Experience collaborating with risk, compliance, model risk management, and internal audit; comfort with control design, evidence, and auditability.