Sr Manager of Software Engineering - Analytics & Reporting Lead

JPMorganChase•Columbus, OH

About The Position

As a Senior Manager of Software Engineering at JPMorganChase within Corporate Technology, you provide technical leadership, coaching, and advisory support to multiple technical teams. You anticipate needs and dependencies across the firm, and use your expertise to influence budget and technical decisions that improve operational efficiency and functionality. You lead a team of entry-level to mid-level software engineers working on basic to moderately complex tasks. You establish and scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across teams, with measurable expectations for delivery speed, quality, and operational outcomes. You promote consistent validation, security, resiliency, and reuse of proven patterns and existing firm technology assets. The role also involves coordinating stakeholders, managing issues and work execution, contributing to resource and budget decisions, and helping teams safely adopt AI-assisted engineering tools and workflows. You are expected to coach engineering leaders on responsible AI use, including data sensitivity, security, resiliency, and control expectations.

Requirements

Formal training or certification in software engineering concepts, with at least 5 years of applied experience.
At least 2 years of experience leading technologists in managing and solving complex technical items within the relevant domain.
Ability to guide and coach teams toward goals aligned with strategic initiatives.
Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance and ways of working, human-in-the-loop validation, quality gates, outcome measurement, and secure handling of sensitive inputs and outputs.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, resiliency and security implications, and control expectations.
Experience hiring, developing, and recognizing talent.
In-depth knowledge of the services industry and its IT systems.
Practical cloud-native experience.
Experience working at code level.

Nice To Haves

Experience building data-driven products such as analytics, insights platforms, dashboards, or experimentation capabilities.
Experience with machine learning frameworks and APIs, and designing or implementing RAG pipelines or agentic AI workflows.
Familiarity with AWS networking, including VPCs, subnets, routing, and security groups; Terraform-based infrastructure deployments; and cost optimization strategies such as autoscaling, spot versus on-demand capacity, and auto-termination.
Experience with RDBMS, NoSQL, and vector databases, and modernizing legacy systems while managing technical debt.
Experience partnering closely with Product and Design, establishing UX standards, and championing customer-centric, accessible design practices across engineering teams.
Observability experience for data systems, including freshness and completeness metrics, lineage, SLAs, and alerting.
Experience with Databricks governance, including Unity Catalog, catalogs and grants, and auditing.
Familiarity with the financial services industry.

Responsibilities

Provide overall direction, oversight, and coaching for entry-level to mid-level software engineers working on basic to moderately complex tasks.
Make decisions affecting team resources, budget, tactical operations, and the execution and implementation of processes and procedures.
Set and scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams.
Establish measurable expectations, such as throughput, defect reduction, and reliability, and ensure consistent validation, security, resiliency, and reuse of proven patterns.
Apply knowledge of Software Development Life Cycle toolchain capabilities, including authorized AI-assisted development and automation tools, to improve efficiency and support capacity initiatives across teams.
Prioritize reuse of existing firm technology assets.
Ensure collaboration across teams and stakeholders.
Identify and mitigate issues affecting execution of the team's work, escalating issues as necessary.
Provide leadership with input on budget, approach, and technical considerations to improve operational efficiency and functionality.
Coach managers, leads, and teams on responsible and secure scaling of AI-assisted engineering practices.