Description 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 at JPMorganChase within the Compute Infrastructure Platforms organisation 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 Generates data models for their team using firmwide tooling, statistics, and contextual analysis Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way 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 Adds to team culture of diversity, opportunity, inclusion, and respect Required qualifications, capabilities, and skills Five years of relevant working experience with both relational and NoSQL databases Experience and proficiency across the data lifecycle Experience with database back-up, recovery, and archiving strategy Experience architecting and managing data solutions on major cloud platforms (AWS, Azure, Google Cloud). Demonstrated ability to implement and oversee data governance frameworks, including regulatory compliance. Hands-on experience designing, building, and optimizing complex ETL/ELT pipelines for large-scale, distributed data systems. Preferred qualifications, capabilities, and skills Proven track record in database performance optimization, including query tuning, indexing strategies, and resource management for both relational and NoSQL systems. Experience leading technical teams, mentoring junior engineers, and fostering collaborative, inclusive environments. Ability to work closely with business stakeholders, data scientists, and software engineers to deliver integrated data solutions. Strong skills in documenting data models, architecture decisions, and operational procedures for knowledge sharing and compliance. Familiarity with CI/CD pipelines, automated testing frameworks, and monitoring tools relevant to data engineering. Familiarity with modern enterprise level compute infrastructure including virtualised and cloud solutions. Familiarity with Databricks, Parquet, Iceberg and, or other high volume solutions.