Lead Software Engineer - Data Governance Engineer Lead

JPMorgan Chase & Co.; Plano (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

JPMorgan Chase & Co. in Plano, TX seeks a Lead Software Engineer to drive data governance and scalable data platforms across the enterprise. You will lead an Agile team to design and implement governance-first data pipelines, ensuring security, reliability, and compliance.

Role emphasizes AI-assisted engineering, modern data tools, and collaboration with cross-functional partners to deliver trusted, market-leading technology products.

Qualifications

• Experience leading data governance initiatives across enterprise systems.
• Strong background in ETL/ELT development and data integration.
• Familiarity with AI-assisted development and secure coding practices.
• Deep knowledge of data architecture and modeling patterns.

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 to improve code quality, delivery speed, and outcomes, with validation standards and reuse of patterns.
• Applies knowledge of SDLC tools, including AI-assisted development, to enhance automation value.
• Create and maintain enterprise data models (conceptual, logical, physical) to support analytics.
• Define, document, and maintain metadata standards and data dictionaries for consistent data usage.
• Implement and administer data cataloging capabilities and ensure data lineage tracking.
• Build and maintain governed ETL/ELT pipelines aligned to governance requirements.
• Implement data quality controls including profiling, rules, monitoring, and remediation workflows.
• Collaborate with architecture, analytics, and compliance to ensure sustainable governance controls.

Skills

Data Governance

ETL/ELT Development

AI-assisted Development Tools

Data Modeling

Data Architecture

Databricks

Snowflake

Cloud Data Platforms

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 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.