Lead Data Engineer - PySpark/Databricks/SQL/AWS

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference. As a Lead Data Engineer - PySpark/Databricks/SQL/AWS at JPMorganChase within the Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements. Generates data models for the team using firmwide approaches, linear algebra, statistical and geometrical algorithms Implements database back-up, recovery, and archiving strategy Evaluates and reports on access control processes to determine effectiveness of data asset security with minimal supervision Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations. Required qualifications, capabilities, and skills Formal training or certification on data engineering concepts and 5+ years applied experience Strong experience with Python, PySpark, Databricks, SQL, AWS and AI automation Proficient in and experience across the data lifecycle Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity. Ability to review and validate AI-assisted outputs (e.g., model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements. Working experience with both relational and NoSQL databases Experience implementing database back-up, recovery, and archiving strategy Proficient knowledge of linear algebra, statistical and geometrical algorithms Preferred qualifications, capabilities, and skills Emerging technologies