Job
Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible. As a Data Platform Engineering Lead at JPMorgan Chase within Asset and Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm s business objectives. Job responsibilities Lead the design, development, and implementation of scalable data pipelines and ETL batches using Python/PySpark on AWS. Execute standard software solutions, design, development, and technical troubleshooting Use infrastructure as code to build applications to orchestrate and monitor data pipelines, create and manage on-demand compute resources on cloud programmatically, create frameworks to ingest and distribute data at scale. Manage and mentor a team of data engineers, providing guidance and support to ensure successful product delivery and support. Collaborate proactively with stakeholders, users and technology teams to understand business/technical requirements and translate them into technical solutions. Optimize and maintain data infrastructure on cloud platform, ensuring scalability, reliability, and performance. Implement data governance and best practices to ensure data quality and compliance with organizational standards. Monitor and troubleshoot application and data pipelines, identifying and resolving issues in a timely manner. Stay up-to-date with emerging technologies and industry trends to drive innovation and continuous improvement. Add to team culture of diversity, equity, inclusion, and respect. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Experience in software development and data engineering, with demonstrable hands-on experience in Python and PySpark. Proven experience with cloud platforms such as AWS, Azure, or Google Cloud. Good understanding of data modeling, data architecture, ETL processes, and data warehousing concepts. Experience or good knowledge of cloud native ETL platforms like Snowflake and/or Databricks. Experience with big data technologies and services like AWS EMRs, Redshift, Lambda, S3. Proven experience with efficient Cloud DevOps practices and CI/CD tools like Jenkins/Gitlab, for data engineering platforms. Good knowledge of SQL and NoSQL databases, including performance tuning and optimization. Experience with declarative infra provisioning tools like Terraform, Ansible or CloudFormation. Strong analytical skills to troubleshoot issues and optimize data processes, working independently and collaboratively. Experience in leading and managing a team/pod of engineers, with a proven track record of successful project delivery. Preferred qualifications, capabilities, and skills Knowledge of machine learning model lifecycle, language models and cloud-native MLOps pipelines and frameworks is a plus. Familiarity with data visualization tools and data integration patterns.
