Senior Manager of Software Engineering

JPMorgan Chase

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Wilmington, DE
Posted: 41 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $214k

$154k most similar roles pay here $267k

This listing doesn't post a salary. Most similar roles pay $182,650–$244,562.

Based on 238 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

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At a glance

TL;DR · Senior Manager of Software Engineering

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As a Sr Manager of Software Engineering within the Corporate Technology – Treasury & CIO Finance Technology team, you will lead and coach multiple engineering teams to develop platforms powering internal funding and Net Interest Income. You will manage technical strategy, oversee resource allocation, and drive the adoption of enterprise-authorized AI-assisted engineering and SDLC automation across various pipelines. Your daily work involves coordinating with Finance, Risk, and Treasury stakeholders to deliver secure, scalable solutions while ensuring finance-grade controls like data lineage and reconciliations meet accounting standards. You will champion best practices for microservices using Java and Python, big data processing via Databricks and Spark, and cloud-native infrastructure in AWS. The role focuses on modernizing platforms through cloud-first and data-first strategies to solve complex problems involving accruals, interest calculation logic, and automated controls within the financial technology domain.

What does a Software Engineering Manager earn?

Median $223750 from 105 postings across 39 companies.

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Skills

Java Python AWS Databricks Spark Microservices API Design CI/CD AI ML Data Pipelines Cloud-native SDLC Observability Big Data Processing Platform Engineering

What you'll do

Provide direction, oversight, and coaching to multiple teams of entry- to mid-level software engineers.
Manage accountability for team resources, budget, tactical operations, and the execution of processes across multiple pipelines.
Scale operating practices for enterprise-authorized AI-assisted engineering and SDLC automation to improve delivery speed and quality.
Drive platform strategy and modernization initiatives focused on cloud-first, data-first, and AI-enabled automation.
Champion engineering best practices for big data processing, microservices, API design, and CI/CD pipelines.
Manage finance-grade controls including data lineage, reconciliations, and validations to meet accounting standards.
Identify and mitigate risks to execute the book of work while proactively driving remediation plans.
Mentor and develop engineering talent through coaching and leadership in technical domains.

What we're looking for

Formal training or certification in software engineering concepts and 5+ years of applied experience.
At least 2 years of experience leading technologists to manage and solve complex technical items within a domain of expertise.
Experience leading multiple teams and coaching them toward goals aligned with strategic initiatives and regulatory timelines.
Experience leading multi-team adoption of enterprise-authorized AI-assisted development tools, including defining governance and quality gates.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and security implications.
Proven track record in hiring, developing, and mentoring engineering talent.
Deep understanding of finance and accounting concepts related to internal funding and Net Interest Income.
Proficiency with Databricks/Spark for large-scale data processing, Python, Java, and AWS cloud-native technologies.
Experience producing high-quality code and design at a senior level (preferred).
Background in AI/ML enablement for operational efficiency and anomaly detection in finance data (preferred).
Familiarity with finance technology operating models and audit/regulatory expectations (preferred).
Demonstrated success driving platform modernization and cloud migration while scaling engineering practices (preferred).

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