Lead Data Engineer

As a Lead Data Engineer (Forward Deployed) at JPMorganChase within the Infrastructure Data Platforms team, you will embed with infrastructure product teams to turn how asset and configuration data exists today into trusted, governed data products that power security controls and enterprise analytics. You will partner directly with teams across compute, network, storage, and cloud to close data visibility gaps and strengthen the firm’s ability to detect and respond to emerging threats.
Job Responsibilities
Embed with infrastructure product teams to discover current-state data sources, ownership, definitions, formats, and quality gaps, and translate findings into a measurable enablement plan
Design and deliver integrations that publish governed data products into a data mesh ecosystem, ensuring completeness, standardization, and lineage
Establish data quality rules and monitoring at the source, driving remediation and preventing recurring issues through root-cause analysis and durable fixes
Standardize critical data attributes and definitions across domains to enable reliable downstream consumption, interoperability, and policy enforcement
Define and implement data contracts that make producer/consumer expectations explicit and reduce operational risk for dependent teams
Reconcile and certify infrastructure asset inventories to close completeness and accuracy gaps that create security and control exposure
Partner with product, engineering, and governance stakeholders to align on authoritative sources, stewardship, and decision rights for key infrastructure datasets
Maintain strong metadata management practices (cataloging, lineage, and stewardship signals) to support auditability and operational transparency
Required Qualifications, Capabilities, and Skills
Formal training or certification on data engineering concepts and 5+ years applied experience
5+ years of hands-on data engineering experience spanning data modeling, data pipeline development, and data quality engineering
Proficiency in Python and SQL, with the ability to build reliable, testable data transformations and integrations
Demonstrated experience diagnosing data quality issues (completeness, accuracy, timeliness, consistency) and implementing controls to prevent recurrence