Lead Software Engineer - Data Governance Engineer Lead

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As a Lead Software Engineer at JPMorgan

Chase 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.

• 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 maintain metadata standards, including business 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 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.

• 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,Power Designer
  , or similar.
• Ability to design transactional and analytical models aligned to business requirements.

• 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.