Are you ready to push the limits of what’s possible in data engineering? At JPMorganChase, you’ll have the opportunity to impact your career and work in an environment that values innovation and collaboration. You’ll 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)