Data Engineer II - AWS

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

• Designs, develop, and maintain scalable data pipelines on AWS using services such as Glue, EMR, Lambda, and Redshift.
• Collaborates with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions.
• Implements ETL processes to ingest, transform, and load data from various sources into cloud-based data warehouses.
• Optimizes data workflows for performance, reliability, and cost efficiency.
• Ensures data quality, integrity, and security across all stages of the data lifecycle.
• Monitors, troubleshoot, and resolve issues in production data pipelines.
• Documents data architecture, processes, and best practices for team knowledge sharing.

• Formal training or certification on data engineering concepts and 2+ years applied experience
• Experience in data engineering, with a focus on cloud-based solutions (AWS).
• Strong proficiency in SQL and Python.
• Experience with building and managing ETL pipelines.
• Familiarity with data modeling, warehousing concepts, and big data technologies.
• Knowledge of data governance, security, and compliance best practices.
• Excellent problem-solving skills and ability to work independently or in a team environment.
• Strong communication skills for technical and non-technical audiences.
• Tools : AWS Glue, AWS Lambda, AWS EMR, AWS Redshift, Amazon S3, Pub/Sub Services: Amazon SNS, SQS, MSK, Apache Spark, Apache Airflow, Python, SQL, Git, CI/CD tools (e.g., Jenkins, AWS CodePipeline), Monitoring tools (e.g., CloudWatch, Datadog)