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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
- 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.
Skills Required
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Expert in distributed data processing framework (Spark)
- Expert in cloud data lakehouse platforms (AWS Data Lake services or Databricks or Hadoop)
- Expert in scheduling/orchestration tools (Airflow, AWS Step Functions or similar)
- Expert with relational and NoSQL databases
- Expertise in data structures, serialization formats (JSON, AVRO, Protobuf) and big-data storage formats (Parquet, Iceberg)
- Proficiency in Python and SQL and experience with at least one additional language (e.g., Java or Scala)
- Hands-on experience utilizing Apache Spark for large-scale batch and real-time data processing
- Proficiency in microservices architecture, serverless computing, and distributed cluster tools (Docker, Kubernetes)
- Experience with data modeling techniques (Dimensional, Data Vault, Kimball, Inmon, etc.)
- Experience with test-driven or behavior-driven development and CI/CD tooling
- Experience leading design workshops, coding sessions, and promoting reusable architecture patterns
- Expertise with streaming platforms like Kafka or MQ
- Demonstrated experience leading use of approved AI-assisted software development tools and validating outputs
- Strong understanding of responsible AI use, data sensitivity, secure handling, resiliency and security expectations
- Hands-on experience with Infrastructure as Code tools (preferably Terraform); AWS CloudFormation valued
- Proficiency with cloud-based data pipeline technologies such as Spinnaker or similar platforms
- Strong working knowledge of Snowflake
- Experience in budgeting and resource allocation for data engineering projects
- Proven ability to manage vendor relationships effectively
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
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What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
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