Data Engineer III – Palmos (Enterprise Platforms)

Are you ready to push the limits of whats possible in data engineering At JPMorganChase youll have the opportunity to impact your career and work in an environment that values innovation and collaboration. Youll join a team where your skills are celebrated and your growth is supported. We empower you to solve complex challenges and make a difference across the firm. Discover how you can shape the future of data with us.

As a Data Engineer III in Enterprise Platforms you will join an agile team focused on onboarding enterprise data and building scalable data pipelines on the Palmos platform. You will design develop and maintain secure reliable and scalable data solutions that support analytics reporting and AI/ML use cases across the firm. You will collaborate with domain teams platform engineering and governance partners to deliver high-quality curated datasets aligned to our data mesh architecture and Palmos platform standards. You will play a key role in advancing our data capabilities and driving innovation.

Job Responsibilities:

• Develop workflows and ELT data pipelines using Python Spark/PySpark and Databricks
• Onboard enterprise datasets into Palmos including ingestion transformation and validation of data assets
• Build test and maintain scalable data pipelines and data architectures that support enterprise analytics use cases
• Apply data engineering best practices for performance optimization reliability and maintainability
• Support implementation of data security governance and entitlements frameworks to protect enterprise data
• Use SQL extensively and work with both relational and NoSQL data stores
• Partner with stakeholders to understand data requirements and translate them into production-ready solutions
• Apply SDLC practices including CI/CD testing and operational monitoring to ensure pipeline stability
• Contribute to reusable frameworks and standards to accelerate onboarding and pipeline delivery
• Identify data issues anomalies and optimization opportunities to improve data quality and performance
• Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality delivery speed and productivity across complex deliverables (e.g. code generation/refactoring unit test creation documentation) while validating outputs through peer review automated testing and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness

• Apply 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:

• Hands-on experience with Databricks Spark/PySpark Python and SQL
• Experience developing and maintaining data pipelines and data processing systems
• Understanding of the data lifecycle including ingestion transformation storage and consumption
• Knowledge of cloud platforms (AWS) and distributed data processing
• Experience with SDLC practices including CI/CD testing and deployment
• Strong problem-solving skills and ability to troubleshoot data and pipeline issues
• Ability to collaborate effectively within agile teams and across stakeholders
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g. for coding test creation troubleshooting or documentation) with demonstrated ability to critically evaluate validate and refine AI-generated outputs for correctness performance and security

• Understanding of responsible AI use in engineering workflows including data sensitivity considerations secure handling of inputs/outputs and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

Preferred Qualifications Capabilities and Skills:

• Experience with Databricks lakehouse Delta Lake and medallion architecture
• Familiarity with enterprise data platforms such as Palmos and data mesh principles
• Exposure to data quality observability and metadata management tools
• Experience supporting analytics reporting or AI/ML workloads
• Experience working within EU regulatory and data protection environments (e.g. GDPR)