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 Corporate Technology, 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:
- Implement and maintain end-to-end data governance solutions that operationalize enterprise data standards, policies, and procedures.
- 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.
- Create and maintain enterprise data models (conceptual, logical, physical) that represent business processes and support analytics.
- Define, document, and maintain metadata standards, including business glossary and data dictionary artifacts to enable consistent data understanding and usage.
- Implement and administer data cataloging capabilities and ensure data lineage tracking from source through transformations to consumption.
- Build and maintain governed ETL/ELT pipelines and patterns that align to governance requirements.
- Implement technical data quality controls, including profiling, rule definition, monitoring, and issue remediation workflows.
- Partner with cross-functional stakeholders (architecture, analytics, compliance) to ensure governance controls are adopted and sustainable.
Required qualifications, capabilities, and skills:
- Expert proficiency in data engineering fundamentals: ETL/ELT development, data integration patterns, and distributed processing.
- 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 knowledge of data architecture and modeling patterns, including dimensional modeling and database design (normalization/denormalization).
- Advanced experience with Databricks , including Delta Lake, Unity Catalog , and Databricks SQL.
- Demonstrated experience with Snowflake , including virtual warehouse optimization, data sharing, and platform security features.
- Proficiency with AWS , especially S3 for data lake implementations (bucket policies, lifecycle management, and service integrations).
- Strong working knowledge of Teradata , including query optimization, workload management, and migration approaches to modern cloud platforms.
- Expert-level data modeling skills (conceptual/logical/physical) using industry-standard methodologies.
- Experience with tools such as Erwin, PowerDesigner , or similar.
- Ability to design transactional and analytical models aligned to business requirements.
Preferred qualifications, capabilities, and skills:
- Advanced ability to profile data, identify quality issues, and implement quality rules and monitoring frameworks.
- Experience implementing data quality capabilities that address accuracy, completeness, consistency, and timeliness.