Lead Data Engineer

Embed with infrastructure teams to turn asset data into trusted, governed products that power security, AI, and threat response.

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 Experience working directly with partner teams Working knowledge of infrastructure or asset-related data domains (e.g., compute, network, storage, cloud) sufficient to model and normalize inventory data Strong problem-solving skills, including the ability to investigate complex data discrepancies across multiple systems and dependencies Comfortable dealing with ambiguity and a fast-changing environment, with the ability to lead and drive effort to completion Familiarity with data contract patterns and practical data quality frameworks (rule definition, monitoring and exception management) Preferred Qualifications, Capabilities, and Skills Experience 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 consumption Experience with semantic modeling across infrastructure layers to connect assets across application, platform, storage, and network contexts Familiarity with graph databases or dependency mapping concepts (e.g., using graph-style modeling to represent relationships between assets) Experience with streaming services like Kafka Familiarity with anomaly detection, pattern analysis, or big data frameworks such as Hadoop/Spark Working knowledge of Java sufficient to contribute to or uplift existing Java-based platforms (e.g., Verum SOR) as needed