Sr. Lead Software Engineer: Data Engineering

JPMorgan Chase & Co.•Columbus, OH

About The Position

Embrace this pivotal role as an essential member of a high performing team dedicated to reaching new heights in data engineering. Your contributions will be instrumental in shaping the future of one of the world’s largest and most influential companies. As a Senior Lead Data Engineer at JPMorganChase within the Employee Data Technology, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics in a secure, stable, and scalable way. Leverage your deep technical expertise and problem solving capabilities to drive significant business impact and tackle a diverse array of challenges that span multiple data pipelines, data architectures, and other data consumers.

Requirements

Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Strong Python coding skills, and deep experience with Databricks and Spark.
Experience with both relational and NoSQL databases
Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs (e.g., model and design summaries or validation recommendations) before use, escalating when uncertain and following data handling requirements.
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
Strong experience with AWS and modern lakehouse and data platform patterns.
Proven technical leadership through architecture decisions, mentoring, and cross-team influence.

Nice To Haves

AWS certification and/or Databricks certification.
Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.

Responsibilities

Provides recommendations and insight on data management and governance procedures and intricacies applicable to the acquisition, maintenance, validation, and utilization of data
Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and design analysis and technical documentation, validating outputs and handling data according to sensitivity and security requirements.
Designs and delivers trusted data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
Defines database back-up, recovery, and archiving strategy
Generates advanced data models for one or more teams using firmwide approaches, linear algebra, statistical and geometrical algorithms
Approves data analysis tools and processes
Creates functional and technical documentation supporting best practices
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
Evaluates and reports on access control processes to determine effectiveness of data asset security
Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., validation automation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations.