Senior Director of Software Engineering, CCB Risk Technology Feature Platform Engineering

JPMorgan Chase

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Plano, TX
Posted: 22 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $215k

$160k most similar roles pay here $274k

This listing doesn't post a salary. Most similar roles pay $174,900–$254,950.

Based on 240 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.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Senior Director of Software Engineering, CCB Risk Technology Feature Platform Engineering

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As a Senior Director of Software Engineering - CCB Risk Technology Feature Platform Engineering, you will own the technical vision and end-to-end delivery of the Consumer and Community Banking Risk Feature Engineering Platform. You will lead multiple departments to build product-oriented streams including the Feature Store, attribute engines, and real-time feature generation. Your role involves setting multi-year architecture for reliability and cost of ownership while driving agentic AI-enabled engineering and SDLC automation. You will manage data governance, quality, and lineage for a regulated platform while partnering with Databricks on roadmap alignment. Required skills include deep architectural command of Databricks, Apache Spark, PySpark, and Delta Live Tables. You must possess expertise in AWS services like EMR, Glue, Lambda, and S3, alongside proficiency in Python, Java, or Scala to manage large-scale batch and real-time data platforms.

What does a Software Engineering Director earn?

Median $262275 from 30 postings across 13 companies.

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Skills

Databricks Apache Spark AWS Terraform Python Java Scala Kafka S3 EMR Glue Lambda ECS EKS Aurora RDS Snowflake SQL NoSQL infrastructure-as-code Feature Store

What you'll do

Define the multi-year technical vision, architecture, and operational ownership for the Risk Feature Engineering Platform.
Lead and manage a team of engineers responsible for feature stores, attribute engines, and real-time feature generation.
Serve as the primary technical authority to resolve complex architectural decisions and set engineering standards.
Develop and scale multi-department strategies for agentic AI-enabled engineering and SDLC automation.
Establish data governance, quality, lineage, and regulatory compliance standards including PCI handling.
Manage the partnership with Databricks to align platform roadmaps and feature parity with firm goals.
Act as the primary interface with senior executives to drive consensus across competing business objectives.
Oversee large-scale batch and real-time data platforms ensuring high availability and low-latency performance.

What we're looking for

Formal training or certification in software engineering concepts and 10+ years of applied experience.
5+ years of experience leading technologists to manage and solve complex technical items within a domain of expertise.
Proven success designing and delivering large-scale batch and real-time data platforms with low-latency serving and strict SLAs.
Deep architectural command of Databricks and Apache Spark at enterprise scale, including Lakeflow, Delta Live Tables, and Unity Catalog.
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 and responsible AI risk management.
Advanced fluency in Python, Java, or Scala, distributed computing, and online/NoSQL stores to lead architecture reviews.
Experience in the financial services industry with knowledge of IT systems and data governance for regulated platforms.
Direct experience standing up or leading an ML feature store or platform at scale (preferred).
Experience migrating legacy Spark/EMR estates to Databricks-native targets (preferred).
Databricks (Data Engineer Professional / Architect) or AWS Solutions Architect certification (preferred).
Familiarity with credit-risk/fraud modeling and Snowflake, Databricks SQL, or lakehouse governance (preferred).

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