What are the responsibilities and job description for the Data Engineer III - Python/AIML 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 JPMorgan Chase within Enterprise Technology, 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
- Design and develop data pipelines to preprocess and transform data for AI/ML solutions.
- Collaborate with cross-functional teams to understand design requirements and translate them into technical solutions.
- Effectively understand and navigate a complex, enterprise technical stack.
- Monitor and maintain deployed models, ensuring their performance and reliability.
- Design and work with cloud-based architectures.
- Support review of controls to ensure sufficient protection of enterprise data.
- Advise and make custom configuration changes in one to two tools to generate a product at the business or customer request.
- Update logical or physical data models based on new use cases.
- Frequently use SQL and understand NoSQL databases and their niche in the marketplace.
- Add to team culture of diversity, equity, inclusion, and respect.
Required qualifications, capabilities, and skills
- Formal training or certification on Data Engineering concepts and 3 years applied experience.
- Experience in software development, including software development life cycle, coding standards, documentation, code reviews, source control management, continuous integration, build processes, testing, and operations experience.
- Demonstrate proficiency in Python, with experience in developing and maintaining production-level code.
- Exhibit proficiency in data engineering, querying various types of data stores, efficiently moving data, working with large datasets, and data preprocessing.
- Have experience with cloud platforms, such as AWS and Azure, for designing and deploying infrastructure to run processes, web apps, and training and inference pipelines for AI/ML models, including handling networking and scaling of these systems.
- Show experience with workflow management and orchestration tools such as Airflow and Kubernetes.
- Possess strong problem-solving and analytical skills.
- Demonstrate excellent documentation, communication, and collaboration skills.
- Have knowledge of infrastructure operations.
- Experience across the data lifecycle.
- Possess significant experience with statistical data analysis and ability to determine appropriate tools and data patterns to perform analysis.
Preferred qualifications, capabilities, and skills
- Have experience in the design or architecture of new and existing systems, including design patterns, reliability, and scaling.
- Demonstrate experience in machine learning engineering.
- Be familiar with DevOps practices for AI/ML model deployment and monitoring.
- Have knowledge of graph databases and familiarity with graph query languages.
