JPMorgan Chase—Lead Software Engineer - Java/Python, AWS, Spark

Job description

Description: We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities - Lead the design, development, and maintenance of robust, scalable cloud-based data processing pipelines and infrastructure, ensuring adherence to engineering standards, governance frameworks, and industry best practices. - Architect and refine data models for large-scale datasets, optimizing for efficient storage, high-performance retrieval, and advanced analytics while upholding data integrity and quality. - Partner with cross-functional teams to translate complex business requirements into effective, scalable data engineering solutions that drive organizational value. - Champion a culture of innovation and continuous improvement, proactively identifying and implementing enhancements to data infrastructure, processing workflows, and analytics capabilities. - Define and execute data strategy, including the development of enterprise data models and the management of end-to-end data infrastructure—from design and construction to installation and ongoing maintenance of large-scale processing systems. - Drive data quality initiatives, ensure seamless data accessibility for analysts and data scientists, and maintain strict compliance with data governance and regulatory requirements. - Align data engineering practices with business objectives, ensuring solutions are both technically sound and strategically relevant. - Author, review, and approve technical requirements and architectural designs, and lead process re-engineering efforts to deliver cost-effective, high-impact business solution - Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. - Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 5+ years applied experience - Expert in at least one distributed data processing framework (Spark). Expert in at least one cloud data Lakehouse platforms (AWS Data lake services or Databricks, if not Hadoop), - Expert in at least one scheduling/orchestration tools ( Airflow, alternatively AWS Step Functions or similar) & Expert with relational and NoSQL databases. Expert in data structures, data serialization formats (JSON, AVRO, Protobuf, or similar), and big-data storage formats (Parquet, Iceberg, or similar) - Hands-on professional experience in one or more programming language(s), including Java or Python, proficiency in Python, SQL, and at least one additional language (e.g. Java or Scala) for data engineering tasks - Hands-on experience utilizing Apache Spark for large-scale data processing, including developing and optimizing data pipelines, performing real-time and batch analytics, and leveraging Spark’s libraries for machine learning and data transformation to drive actionable business insights. - Proficiency in microservices architecture, serverless computing and distributed cluster computing tools such as Docker, Kubernetes etc. Experience in one or more data modelling techniques (Dimensional, Data Vault, Kimball, Inmon, etc.) - Experience with test-driven development (TDD) or behavior-driven development (BDD) practices, as well as working with continuous integration and continuous deployment (CI/CD) tools. - Experience organizing and leading design workshops, coding sessions, and hackathons to promote a culture of excellence and innovation in data engineering. Expertise in architecting reusable, future-ready design patterns that address diverse use cases across the organization. - Expertise in working with streaming platforms like Kafka, MQ etc. - Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security. - Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices Preferred qualifications, capabilities, and skills - Hands-on experience with Infrastructure as Code (IaC) tools, preferably Terraform; experience with AWS CloudFormation is also valued. - Proficiency in cloud-based data pipeline technologies such as Spinnaker or similar platforms. - Strong working knowledge of the Snowflake data platform. - Experience in budgeting and resource allocation for data engineering projects. - Proven ability to manage vendor relationships effectively.