at J.P. Morgan
Bulge Bracket Investment BanksPosted 3 days ago
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**Software Engineer III** As a Software Engineer III at JPMorganChase, you'll lead our data integration strategy – architects and owns the flow of operational data from Snowflake, ServiceNow APIs, and external sources into our Oracle/Elasticsearch platform. Key responsibilities include designing robust data pipelines, optimizing schemas and query performance, and ensuring end-to-end data quality. You'll also develop backend services using Java (Spring Boot) and Python, collaborate with data scientists on AI/ML projects, and maintain code quality using enterprise-authorized AI tools. Proficient in Java, Python, Snowflake, Oracle/PostgreSQL, and agile methodologies. 5+ years of software engineering experience required.
- Compensation
- Not specified
- City
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- Country
- Argentina
Currency: Not specified
Full Job Description
Location: Ciudad Autnoma de Buenos Aires, Argentina
As a Software Engineer III at JPMorganChase you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.
Enigma is an AI-powered incident analytics platform that transforms ITSM data into actionable insights for operational intelligence. We're seeking a Software Engineer III to architect and own our data integration strategydesigning how operational data flows from Snowflake and external sources into our Oracle/Elasticsearch platform. This role requires a well-rounded engineer who excels at data modeling and pipeline design while contributing to backend development. You'll balance building robust data foundations with delivering features that leverage AI/ML capabilities to serve hundreds of users across the organization.
Job responsibilities
- Design and implement data ingestion pipelines from Snowflake, ServiceNow APIs, and external sources into Oracle database with Elasticsearch indexing
- Architect schemas, transformation logic, and data models that optimize for query performance, search relevance, and feature requirements
- Own end-to-end data quality, observability, and performance: freshness checks, schema validation, monitoring/alerting, and clear SLAs
- Establish data accessibility standards via REST APIs and MCP Server interfaces for AI/LLM consumption
- Develop backend services using Java (Spring Boot) and Python to support analytical and AI-powered features
- Collaborate with Data Scientist on vectorization strategies and data preparation for LLM-based summarization and semantic search
- Leverage enterprise-authorized AI tools (Copilot, Claude Code) to accelerate development while maintaining code quality and security standards
- Create documentation, runbooks, and support processes; participate in production deployments and incident triage
- 5+ years software engineering experience with demonstrated expertise in data architecture and backend development
- Strong data modeling and database design skills - complex schemas, query optimization, performance tuning
- Proficiency in Java and Python - production-grade development in both languages
- Hands-on Snowflake experience - SQL, data warehousing patterns, cost/performance optimization
- Oracle or PostgreSQL expertise - indexing strategies, stored procedures, query plan analysis
- Experience building production data pipelines with data quality frameworks and monitoring
- REST API design and development experience
- Spring Boot or similar Java backend framework
- Solid understanding of Software Development Life Cycle and agile methodologies
- Strong problem-solving and communication skills with both technical and business stakeholders
- Elasticsearch or OpenSearch - indexing strategies, query DSL, vector search capabilities
- Experience with AI/ML data preparation, vectorization, or LLM integration
- Familiarity with ITSM/ServiceNow datasets
- Experience building MCP servers or similar integrations connecting AI applications to data sources
- Python data libraries (pandas, numpy)
- Hibernate Search or ORM-based search frameworks
- CI/CD tools (Jenkins, GitHub Actions)
- Angular or modern frontend frameworks
