The role
From JPMorgan Chase's own posting.
Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let’s push the boundaries of what's possible together.
As a Senior Director of Software Engineering at JPMorganChase within the Corporate Technology line of business, you will own the technical vision and end-to-end delivery of the Consumer and Community Banking Risk Feature Engineering Platform. You lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances.
Job responsibilities
Sets the multi-year technical vision and architecture for the Risk Feature Engineering Platform, and is the accountable owner for delivery, reliability, cost of ownership, controls, and portfolio operations
Builds, develops, and holds accountable a team of engineers running product-oriented streams including the Feature Store, attribute engines & migration, and real-time feature generation
Serves as the definitive technical authority for the platform — resolves the hardest architecture and trade-off decisions, sets engineering standards, and raises the technical bar of the leadership bench beneath them
Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments
Sets the data-governance, data-quality, and lineage direction across the platform and its regulatory posture, including PCI handling
Partners directly with Databricks on platform roadmap, GA timelines, and feature parity, and aligns the Risk Feature Store with the firm's broader declarative and feature-platform direction
Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives
Manages multiple stakeholders, complex projects, and large cross-product collaborations
Influences peer leaders and senior stakeholders across the business, product, and technology teams
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
A demonstrated record as a technical leader who sets direction, not just execution — someone who has personally architected large-scale platforms and can still go deep enough to earn the trust of senior engineers
Deep architectural command of Databricks and Apache Spark at enterprise scale — Lakeflow / Delta Live Tables, Unity Catalog, PySpark, and declarative pipeline patterns — and the judgment to bet a platform's future on the right generation of the technology
Proven success designing and delivering large-scale batch and real-time data/feature platforms in production, including low-latency serving (JVM services, Kafka, modern concurrency) against strict TPS / P99 SLAs
Strong public-cloud engineering foundation on AWS (EMR, Glue, Lambda, S3, ECS/EKS, Aurora/RDS) and infrastructure-as-code with Terraform
Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
Advanced fluency in one or more of Python, Java, or Scala, distributed computing, and online/NoSQL stores — sufficient to lead architecture reviews and adjudicate technical disputes
Demonstrated ownership of data governance, data quality, and lineage for a regulated, large-scale data platform
Experience hiring, developing, and retaining senior engineering leaders; experience partnering with (or serving as) Product ownership on a platform product
In-depth knowledge of the financial services industry and its IT systems, and the credibility to represent a platform to executive stakeholders
Preferred qualifications, capabilities, and skills
Direct experience standing up or leading an ML feature store / feature platform at scale (Databricks Feature Engineering, Feature Views, Mosaic AI Model Serving, or comparable — Tecton, Feast, custom-on-Delta)
Experience migrating a legacy Spark/EMR estate to a Databricks-native, declarative target state across a large engine/pipeline fleet
Databricks (Data Engineer Professional / Architect) and/or AWS Solutions Architect certification
Familiarity with credit-risk / fraud modeling data needs and point-in-time correctness for training pipelines
Snowflake, Databricks SQL, and lakehouse governance (Unity Catalog fine-grained access, PCI data handling)
Data encryption & key management best practices, especially AWS KMS