Data Engineer III - Senior Associate

As a Data Engineer III at JPMorgan Chase, you will be a key member of our agile Data Engineering team, designing and delivering trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You will develop, test, and maintain critical data pipelines and architectures supporting the firm’s business objectives, with a primary focus on AWS Data Engineering and Databricks.

In this role, you will have autonomy to lead projects, mentor junior engineers, and collaborate closely with Finance and Operations teams to translate cost optimization strategies into technical solutions. Your ideas and contributions will be valued and considered as part of our team’s ongoing innovation.

Job Responsibilities:

• Design, develop, and maintain scalable ETL pipelines using AWS services (Lambda, Step Functions, S3 storage) and Databricks (Python, PySpark, SQL).
• Optimize data processing workflows leveraging Apache Spark and Delta Lake within Databricks.
• Integrate data from various sources (S3, databases, APIs) into Databricks and AWS data stores.
• Implement and manage data governance, security controls, and access policies using AWS IAM, encryption, and Databricks Unity Catalog.
• Monitor, troubleshoot, and tune data pipelines for performance and reliability.
• Automate infrastructure provisioning and deployment using Terraform or AWS CloudFormation.
• Collaborate with cross-functional teams using Databricks Repos and version control tools.
• Work closely with FinOps professionals to apply cost optimization strategies and best practices in cloud environments.
• Mentor junior engineers and provide technical guidance within the team and advise colleagues on data engineering best practices and tool configurations.
• Update logical or physical data models based on new use cases.
• Communicate complex technical concepts to non-technical stakeholders.

Required Qualifications, Capabilities, and Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Extensive experience across the data lifecycle, including data ingestion, transformation, storage, and analytics.
• Advanced proficiency in SQL (joins, aggregations) and working understanding of NoSQL databases.
• Significant experience with statistical data analysis and ability to determine appropriate tools and data patterns.
• Hands-on experience with AWS data engineering services (Lambda, S3, Redshift, RDS, DynamoDB).
• Experience implementing data governance and security controls in cloud environments.
• Proficiency in workflow orchestration and automation (AWS Step Functions, Airflow).
• Experience with infrastructure as code (Terraform, CloudFormation).
• Strong problem-solving, communication, and collaboration skills.
• Ability to mentor junior engineers and lead projects with autonomy.

Preferred Qualifications, Capabilities, and Skills:

• Experience with Databricks, including ETL development, Spark optimization, and Delta Lake.
• Familiarity with Databricks Unity Catalog and Repos.
• Notion of FinOps and cloud cost optimization; willingness to work closely with FinOps professionals.
• Experience with monitoring tools (AWS CloudWatch, CloudTrail) and cost management dashboards.
• Financial services industry experience is a plus.