What are the responsibilities and job description for the Data Engineer III - Python / AWS / SQL position at JPMorgan Chase?
Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Data Engineer III at JPMorganChase within the Consumer & Community Banking - Wealth Management team, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Collaborates with data governance teams to ensure data quality, lineage, and compliance with enterprise standards.
- Develops and maintains automated testing and monitoring for data pipelines to ensure reliability and accuracy.
- Optimizes data storage and query performance for large-scale datasets.
- Updates logical or physical data models based on new use cases.
- Frequently uses SQL and understands NoSQL databases and their niche in the marketplace.
- Supports review of controls to ensure sufficient protection of enterprise data.
- Adds to team culture of diversity, opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills
- Formal training or certification on data engineering concepts and 3 years applied experience
- Advanced proficiency in SQL (e.g., joins and aggregations) and working understanding of NoSQL databases
- Proficiency programming in Python for data processing tasks
- Proficient in Object-Oriented Programming (OOP) concepts, with a strong ability to design and implement robust, reusable, and maintainable code structures across various programming languages.
- Experience building and consuming RESTful APIs for data integration. Familiarity with event-driven architectures.
- Experience with query optimization, partitioning, and indexing strategies for large datasets.
- Extensive experience with cloud platforms to design, deploy, and manage scalable and efficient cloud-based solutions.
- Hands-on experience with distributed data processing frameworks, leveraging their capabilities for large-scale data processing and analytics to drive efficient and insightful data solutions.
- Experience in utilizing behavior-driven development (BDD) frameworks.
Proficiency in Unix scripting, data structures, data serialization formats such as JSON, AVRO, or similar, and big-data storage formats such as Parquet.
- Understanding of data security best practices and compliance requirements.
Preferred qualifications, capabilities, and skills
- Familiarity with CI/CD pipelines, containerization, and orchestration technologies.
- Provision infrastructure using a high-level configuration language.
- Experience with monitoring and analyzing system performance.
- Experience with real-time monitoring and performance analysis of applications and infrastructure.
- Exposure to integrating machine learning models or AI-driven analytics into data pipeline
- Familiarity with AI/ML platforms or frameworks for building, deploying, or managing machine learning solutions.