Lead Data Engineer (Infrastructure Data Platforms)

Lead Data Engineer (Forward Deployed)As a Lead Data Engineer 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 ResponsibilitiesEmbed with infrastructure product teams to discover current-state data sources, ownership, definitions, formats, and quality gaps, and translate findings into a measurable enablement planDesign and deliver integrations that publish governed data products into a data mesh ecosystem, ensuring completeness, standardization, and lineageEstablish data quality rules and monitoring at the source, driving remediation and preventing recurring issues through root-cause analysis and durable fixesStandardize critical data attributes and definitions across domains to enable reliable downstream consumption, interoperability, and policy enforcementDefine and implement data contracts that make producer/consumer expectations explicit and reduce operational risk for dependent teamsReconcile and certify infrastructure asset inventories to close completeness and accuracy gaps that create security and control exposurePartner with product, engineering, and governance stakeholders to align on authoritative sources, stewardship, and decision rights for key infrastructure datasetsMaintain strong metadata management practices (cataloging, lineage, and stewardship signals) to support auditability and operational transparencyRequired Qualifications, Capabilities, and SkillsFormal training or certification on data engineering concepts and 5+ years applied experience5+ years of hands-on data engineering experience spanning data modeling, data pipeline development, and data quality engineeringProficiency in Python and SQL, with the ability to build reliable, testable data transformations and integrationsDemonstrated experience diagnosing data quality issues (completeness, accuracy, timeliness, consistency) and implementing controls to prevent recurrenceExperience working directly with partner teamsWorking knowledge of infrastructure or asset-related data domains (e.g., compute, network, storage, cloud) sufficient to model and normalize inventory dataStrong problem-solving skills, including the ability to investigate complex data discrepancies across multiple systems and dependenciesComfortable dealing with ambiguity and a fast-changing environment, with the ability to lead and drive effort to completionFamiliarity with data contract patterns and practical data quality frameworks (rule definition, monitoring and exception management)Preferred Qualifications, Capabilities, and SkillsExperience with IT asset management or configuration management concepts (e.g., asset inventories, configuration management databases)Exposure to data mesh and data product operating models, including publishing reusable datasets for broad consumptionExperience with semantic modeling across infrastructure layers to connect assets across application, platform, storage, and network contextsFamiliarity with graph databases or dependency mapping concepts (e.g., using graph-style modeling to represent relationships between assets)Experience with streaming services like KafkaFamiliarity with anomaly detection, pattern analysis, or big data frameworks such as Hadoop/SparkWorking knowledge of Java sufficient to contribute to or uplift existing Java-based platforms (e.g., Verum SOR) as needed