Senior Manager of Software Engineering - Research & Development NEO and BRIE Platforms

Senior Manager of Software Engineering - Research & Development for the NEO and BRIE Platforms

Jersey City, NJ, United States

Base Pay: $ - $

Job Schedule:

Full time

Job Shift: Day

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 – Research & Development for the NEO and BRIE Platforms at JPMorgan Chase within the Commercial and Investment Banking – Data Analytics Payment Team
, you lead multiple engineers and set the technical direction for how emerging technologies are evaluated, proven, and adopted across our agent runtime and data platform estate. You manage the team’s output, practices, and collaboration, and you are responsible for anticipating the needs of the platforms and the stakeholders they serve in a secure, stable, and scalable way.

• Provides overall direction, oversight, coaching, and career development for a team of 4–5 software engineers.
• Owns the research and development agenda for the NEO agent runtime and BRIE data platforms.
• Leads structured technology evaluations and proofs of concept, defining success criteria and benchmarking candidates.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices.
• Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation.
• Applies knowledge of tools within the Software Development Life Cycle toolchain to drive efficiency and capacity unlock initiatives.
• Reviews and debugs code and designs authored by the team, remaining hands‑on enough to guide critical technical decisions.
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes‑oriented probing of architectural designs.
• Makes decisions that influence team resources, budget, and tactical operations, and is accountable for those outcomes.
• Ensures successful collaboration across engineering teams, product, and platform stakeholders, and communicates findings and trade‑offs to senior leadership.
• Adds to team culture of diversity, opportunity, inclusion, and respect.

• Formal training or certification on software engineering concepts and 5+ years applied experience, with 2+ years leading and coaching teams of technologists.
• Hands‑on practical experience delivering system design, application development, testing, and operational stability.
• Experience hiring, developing, and recognizing talent, and setting expectations for team output and engineering practices.
• Advanced proficiency in at least one programming language:
  Java, Python, or Rust.
• Demonstrated experience leading effective use of approved AI‑assisted software development tools.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations and secure handling of inputs/outputs.
• Experience leading multi‑team adoption of enterprise‑authorized AI‑assisted development and delivery tools.
• Proficient in all aspects of the Software Development Life Cycle.
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
• Proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, distributed data systems).

In‑depth knowledge of the financial services industry and their IT systems.

• Experience evaluating and operating query federation engines (e.g., Starburst/Trino) across heterogeneous data sources.
• Experience with centralized policy and authorization engines (e.g., OPA/Rego, OpenFGA) for runtime and structural access control.
• Experience deploying and tuning columnar/OLAP stores (e.g., Click House) in both on‑premises and AWS environments.
• Familiarity with open‑source data catalog solutions and open table formats (Apache Iceberg) for lakehouse architectures.
• Experience with caching and in‑memory data solutions (e.g., Redis) for low‑latency retrieval and agent/session memory.
• Exposure to LLMs, RAG architectures, vector…