Lead Software Engineer - Data Governance Engineer Lead

JPMorgan Chase & Co.; Plano (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. within Corporate Technology seeks a Lead Software Engineer to drive data governance initiatives and AI-assisted coding practices in a secure, scalable tech environment.

You will build data models and metadata standards, while coordinating with architecture, analytics, and compliance teams. You will implement end-to-end data governance pipelines, maintain data catalogs and lineage, and apply best practices across the SDLC to enhance code quality and delivery speed.

Qualifications

• Expert in ETL/ELT development and data integration.
• Experience leading AI-assisted software development with coding, review, and testing.
• Understanding of responsible AI and secure data handling.

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 maintainenterprise data models(conceptual, logical, physical) that represent business processes and support analytics.
• Define, document, and maintainmetadata standards, includingbusiness glossaryanddata dictionaryartifacts to enable consistent data understanding and usage.
• Implement and administerdata catalogingcapabilities and ensuredata lineage trackingfrom source through transformations to consumption.
• Build and maintain governedETL/ELT pipelinesand patterns that align to governance requirements.
• Implement technicaldata 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.

Skills

ETL/ELT development

Data integration

Distributed processing

AI-assisted development

Data governance

Responsible AI

Data modeling

Dimensional modeling

Databricks

Snowflake

AWS S3

Teradata

Erwin

PowerDesigner

Data architecture

Tools

Databricks

Delta Lake

Unity Catalog

Databricks SQL

Snowflake

AWS S3

Teradata

Erwin

PowerDesigner

Job description

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 maintainenterprise data models(conceptual, logical, physical) that represent business processes and support analytics.
• Define, document, and maintainmetadata standards, includingbusiness glossaryanddata dictionaryartifacts to enable consistent data understanding and usage.
• Implement and administerdata catalogingcapabilities and ensuredata lineage trackingfrom source through transformations to consumption.
• Build and maintain governedETL/ELT pipelinesand patterns that align to governance requirements.
• Implement technicaldata 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 withDatabricks, includingDelta Lake,Unity Catalog, andDatabricks SQL.
• Demonstrated experience withSnowflake, including virtual warehouse optimization, data sharing, and platform security features.
• Proficiency withAWS, especiallyS3for data lake implementations (bucket policies, lifecycle management, and service integrations).
• Strong working knowledge ofTeradata, 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 asErwin,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.