You thrive on diversity and creativity, and we welcome individuals who share our vision of making a lasting impact. Your unique combination of design thinking and experience will help us achieve new heights.

  As a Data Engineer II at JPMorgan Chase within the Asset & Wealth Management, you are part of an agile team that works to enhance, design, and deliver the data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As an emerging member of a data engineering team, you execute data solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities

 

Organizes, updates, and maintains gathered data that will aid in making the data actionable. Enhance data protection controls to ensure the security and integrity of enterprise data. Demonstrates basic knowledge of the data system components to determine controls needed to ensure secure data access. Utilize SQL extensively and maintain proficiency in NoSQL databases, understanding their unique roles within the industry. Be responsible for making custom configuration changes in one to two tools to generate a product at the business or customer request Updates logical or physical data models based on new use cases with minimal supervision Adds to team culture of diversity, equity, inclusion, and respect

 

Required qualifications, capabilities, and skills

 

Formal training or certification on Data engineering concepts and 2+ years applied experience Proven experience managing the full lifecycle of data, from collection and storage to analysis. Proficiency in Python for data manipulation and analysis. Experience with major cloud platforms such as AWS and Snowflake. Familiarity with ETL tools, including Spark and Databricks. Extensive knowledge of database management systems, including Oracle and MSSQL. Demonstrated ability in statistical data analysis, selecting appropriate tools and identifying relevant data patterns for comprehensive analysis. Understanding of SQL and NoSQL database concepts. Knowledge of Data Mesh, data modeling, and domain-driven design. Experience with version control systems and tools, including Git, GitHub, GitLab, and Bitbucket. Experience implementing custom changes in tools to produce tailored data products.