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 Corporate Technology – Treasury & CIO (TCIO) Finance Technology team, you will lead and coach multiple engineering teams while shaping the technical strategy for platforms that power internal funding and Net Interest Income (NII). You will anticipate cross-functional dependencies across Finance, Risk, Treasury, and Infrastructure, and your insights will inform budget and technical decisions that advance efficiency, resiliency, and control.
Job responsibilities
- Provide direction, oversight, and coaching to teams of entry- to mid-level software engineers delivering solutions for internal funding, NII, and related accounting/finance use cases.
- Manage accountability for decisions impacting team resources, budget, tactical operations, and the execution of processes and procedures across multiple pipelines and services.
- Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
- Ensure successful collaboration across Finance, Risk, Treasury, SRE, and Data Platform stakeholders to deliver secure, scalable, and well-controlled solutions.
- Identify and mitigate issues to execute the book of work; escalate risks proactively and drive remediation plans.
- Manage platform strategy and modernization (cloud-first, data-first), including AI enablement for efficiency, anomaly detection, and automation of controls.
- Champion engineering best practices for big data processing (Databricks, Spark), microservices (Java/Python), API design, CI/CD, observability, and resiliency.
- Manage logical explanations and finance-grade controls (data lineage, reconciliations, validations) that meet accounting and reporting standards.
- Create a culture of diversity, opportunity, inclusion, and respect, with emphasis on mentorship, talent development, and thought leadership.
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
- Experience leading multiple teams; ability to guide and coach on achieving goals aligned to strategic initiatives and regulatory timelines.
- Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
- Proven track record hiring, developing, and recognizing engineering talent; strong mentorship and people leadership.
- Deep understanding of finance and accounting concepts related to internal funding and NII (e.g., accruals, FTP/base rate curves, interest calculation logic, ledger alignment).
- Practical cloud-native experience, in AWS (compute, storage, networking, security).
- Proficiency with Databricks/Spark for large-scale data processing; strong coding skills in Python and Java; familiarity with data pipelines and controls.
- Experience in Computer Science, Engineering, Mathematics, or a related field, with expertise across core technology disciplines (APIs, data, platform engineering).
Preferred qualifications, capabilities, and skills
- Experience producing high-quality code and design at a senior level; ability to set standards, review designs, and drive technical direction.
- Background in AI/ML enablement for operational efficiency, quality controls, and anomaly detection in finance data.
- Familiarity with finance technology operating models, controls, and audit/regulatory expectations; experience coordinating with Finance and Risk stakeholders.
- Demonstrated success driving platform modernization (cloud migration, data platform evolution) while managing day-to-day inefficiencies and scaling engineering practices.