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 JPMorgan Chase within the Firm wide Planning and Analysis Data Platform Team, 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
1. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
2. Develops secure high-quality production code, and reviews and debugs code written by others.
3. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
4. Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
5. Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
6. Adds to team culture of diversity, equity, inclusion, and respect.
Required qualifications, capabilities, and skills
1. Formal training or certification on software engineering concepts and advanced applied experience in Data Management, Data Integration, Data Quality, Data Monitoring, and Analytics experience.
2. Experience leading teams of technologists and managing global stakeholders.
3. Experience in data engineering with proficiency in Python and PySpark.
4. Experience with building Cloud native applications using cloud platforms such as AWS, Azure, GCP and experience in leveraging cloud services for data storage, processing, and analytics.
5. Hands-on experience in data integration and handling projects that involve processing huge volumes of data for reporting models.
6. Hands-on experience in database systems (both SQL and NoSQL) and create/maintain scalable database load processes with framing up Complex SQL Queries and ensuring optimal data storage and retrieval.
7. Expertise in working with agile projects to automate testing/dev ops environments.
8. Knowledge of big data technologies such as Apache Spark or PySpark.
9. Hands-on experience with containerization technologies like Docker and Kubernetes (EKS).
10. Ability to guide and coach teams on approaches to achieve goals aligned against a set of strategic initiatives.
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