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Lead Data Engineernew

JPMorgan Chase (JPMC) · Other

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Closes in 6 days — 21 Aug 2026

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. 

As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 

Job responsibilities
  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
  • Develops secure and high-quality production code, and reviews and debugs code written by others
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
  • Build ensemble-based unsupervised anomaly detection (multiple detectors, score aggregation/calibration, thresholding, suppression/deduping).
  • Develop scalable feature engineering and data pipelines on Databricks (Spark/Delta, workflows/jobs, MLflow) using data from the data lake.

 

Required qualifications, capabilities, and skills
 
  • A. Formal training or certification on software engineering concepts and 5+ years applied experience 
    Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Advanced in one or more programming language(s)
  • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
  • Proficient in all aspects of the Software Development Life Cycle
  • Unsupervised anomaly detection: clustering (KMeans/DBSCAN/HDBSCAN), isolation approaches (Isolation Forest), density/outlier methods (LOF), PCA-based methods, autoencoders, time-series anomaly techniques.
  • Ensembling & scoring: rank/score aggregation, calibration, dynamic baselines, threshold optimization, drift-aware tuning.
  • Databricks: Spark, Delta, MLflow, notebooks, workflows/jobs, performance tuning.
  • Strong Python and SQL; experience with distributed data processing.

 

Preferred qualifications, capabilities, and skills
 
  • Experience of communicating and engaging with senior leadership/stakeholders - 
  • Advanced understanding of agile methodologies such as CI/CD, Applicant Resiliency, and Security - 
  • Familiarity with API and Micro services frameworks, Container Technologies, and workflows

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