Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorgan Chase within Commercial and Investment 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. You will design and deliver cloud-based BI and analytics solutions on large datasets, improve data performance and reliability, and use modern tooling (including AI-assisted development) to speed up high-quality delivery. Your expertise in various technology domains will be counted on to set strategic direction and solve complex and mission critical problems, internally and externally. You will also drive significant business impact through your capabilities and contributions and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Our Commercial and Investment Bank relies on engineers to build and run the platforms that help corporations, governments, and institutions operate safely at global scale. You’ll develop solutions for a bank entrusted with holding $18 trillion of assets and $393 billion in deposits. Commercial & Investment Bank provides strategic advice, raises capital, manages risk, and extends liquidity in markets spanning over 100 countries around the world.
Job responsibilities
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Builds and evolves cloud-based BI and analytics solutions that turn large, complex datasets into reliable decision-ready insights
- Designs and owns managed ETL/ELT pipelines (e.g., AWS Glue, Airflow) with clear SLAs, monitoring, and cost/performance controls
- Models and maintains data warehouse structures that scale to very large volumes while staying easy to query and extend
- Delivers performant data products by applying query optimization, profiling, and performance monitoring techniques
- Develops dashboards and reporting experiences (e.g., Tableau) that clearly answer business questions and drive action
- Applies AI-enabled development practices (including intelligent agents and automation) to improve productivity and consistency
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
- Adds to the team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- 5+ years of strong experience in the Business Intelligence domain with 5+ years of strong experience in Cloud Technologies (e.g. AWS/Azure/Google Cloud)
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Strong experience in managed ETL modern skills (e.g. Amazon Glue/Airflow).
- Highly proficient and knowledge about data warehousing concepts.
- Experience in designing BI solutions on large datasets with a hands-on experience on modern data visualization tools (e.g. Tableau)
- Understanding data and query optimization, query profiling, and query performance monitoring tools and techniques.
- Extensive knowledge on database design techniques and experience on working with extremely large data volumes at scale.
- Proficient in leveraging AI technologies to enhance coding practices, including the development and deployment of intelligent agents and automation solutions
- Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Ability to tackle design and functionality problems independently with little to no oversight
Preferred qualifications, capabilities, and skills
- Modern data stack experience (e.g., Lakehouse patterns, Delta/Iceberg/Hudi)
- Streaming/near-real-time data pipelines (e.g., Kafka, Kinesis)
- Infrastructure as Code and automated environment provisioning (e.g., Terraform, CloudFormation)
- CI/CD for data and analytics workloads, with automated testing and release gates
- Observability for data pipelines (metrics, logging, lineage, data quality monitoring)
- Containerization and orchestration (Docker, Kubernetes)
- Experience with modern query engines and performance tuning (e.g., Spark, Trino/Presto)