Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Data Engineer at JPMorganChase within the International Consumer Bank, you will serve as a seasoned member of an agile team, designing and delivering trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You will be responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas and business functions in support of the firm's business objectives.

Job responsibilities

• Implement data solutions on cloud platforms, preferably AWS, using services such as S3, EKS, Glue, Lambda, IAM, Athena, and container-based runtime environments.
• Design, build, test, deploy, and maintain full-stack applications supporting a data and analytics product, with a strong focus on operational stability and resiliency.
• Develop and maintain microservices and event-driven architectures, including RESTful APIs and integration patterns for data workflows.
• Design and implement complex, scalable solutions to process data efficiently, ensuring consistent and timely delivery and availability. Focus on building robust systems that can handle large volumes of data with minimal downtime.
• Develop solutions using Agile DevOps methodologies and continuous integration/continuous deployment practices on public cloud platforms.
• Collaborate with key partners to enhance understanding of data usage within the business. Serve as a subject matter expert on the content and application of data in the product and related business areas.
• Support the Data Analytics and Insights team by identifying and integrating necessary data into analytics platforms.
• Document and enforce requirements for data accuracy, completeness, and timeliness within the product.
• Contribute to a team culture that values diversity, opportunity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline.
• Formal training or certification on data engineering concepts with 5+ years of experience.
• Demonstrated expertise in Python programming, as well as hands-on experience with data processing technologies, including Spark or PySpark.
• Strong knowledge of the SDLC and agile practices, including CI/CD, application resiliency, and security.
• Working understanding of NoSQL databases; experience with large-scale datasets, data lakehouse, and data warehouse technologies at least at TB scale, ideally PB scale, with at least one of Databricks, Redshift, Snowflake, or BigQuery.
• Recent hands-on professional experience, actively coding, as a data engineer; back-end software engineering experience will also be considered.
• Understanding of distributed systems and public cloud services, such as AWS or other cloud providers.
• Experience with SQL, any dialect, and data tools, e.g., dbt, Glue, and Trino.
• Experience with Infrastructure as Code, ideally Terraform, for cloud-based data infrastructure.
• Experience with a scheduling system, such as AWS Step Functions or Airflow.

Preferred qualifications, capabilities, and skills

• Experience automating deployments, releases, and testing in continuous integration and continuous delivery pipelines.
• A solid approach to writing unit-level tests using mocking frameworks, as well as automating component, integration, and end-to-end tests.
• Experience with containers and container-based deployment environments, such as Docker and Kubernetes.
• Understanding of data streaming and scalable data processing frameworks, such as Kafka and Spark Structured Streaming.
• Cloud certifications, including AWS Certified Data Engineer – Associate, or AWS Certified Solutions Architect.
• Experience with TypeScript and modern React development practices.