JP Morgan Chase
Data Engineer III - Senior Associate
Senior Data Engineer building and optimizing AWS and Databricks pipelines for JPMorgan Chase, focusing on scalable ETL, Spark/Databricks workflows, and cloud cost governance.
- Compensation
- Not disclosed
- Location
- Ciudad Autónoma de Buenos Aires, Argentina
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- 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 technically and lead projects with autonomy.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
- Advanced English skills
Nice to have
- 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.
Full description
Senior Data Engineer (Data Engineer III) – AWS & Databricks FocusDescription
As a Data Engineer III at JPMorgan Chase Corporate Sector, 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 FinOps professionals 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.
- Update logical or physical data models based on new use cases.
- Advise colleagues on data engineering best practices and tool configurations.
- Communicate complex technical concepts to non-technical stakeholders.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
- Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.