Data Engineer III - Senior Associate

JPMorgan Chase & Co.; Buenos Aires; Sur place; ARS 181 565 000 - 272 348 000; Plein temps

Résumé du poste

JPMorgan Chase & Co. seeks a Senior Data Engineer focused on AWS and Databricks to design scalable data pipelines and analytics solutions. You will own critical ETL work, mentor junior engineers, and collaborate with FinOps to implement cost optimization in cloud environments.

Requirements include extensive SQL, Spark, Python experience and knowledge of Databricks Unity Catalog; strong English skills are essential as part of a global team.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• Extensive experience across the data lifecycle including ingestion, transformation, storage, and analytics.
• Advanced proficiency in SQL and working understanding of NoSQL databases.
• 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).
• Infrastructure as code experience (Terraform, CloudFormation).
• Strong problem-solving, communication, and collaboration skills.
• Ability to mentor junior engineers and lead projects with autonomy.

Responsabilités

• Design, develop, and maintain scalable ETL pipelines using AWS services and Databricks.
• Optimize data processing workflows with Apache Spark and Delta Lake.
• Integrate data from various sources into Databricks and AWS data stores.
• Implement and manage data governance, security controls, and access policies.
• Monitor, troubleshoot, and tune data pipelines for performance.
• Automate infrastructure provisioning and deployment using IaC tools.
• Collaborate with cross-functional teams using Databricks Repos and version control.
• Mentor junior engineers and provide technical guidance.
• Update data models based on new use cases.
• Communicate complex concepts to non-technical stakeholders.
• Leverage AI capabilities with strong validation and data handling compliance.

Connaissances

SQL proficiency

NoSQL databases

AWS data engineering

Terraform

CloudFormation

Airflow

Python

PySpark

Databricks

Data governance

Security controls

Mentoring

English skills

Formation

Bachelor's or Master's degree

Outils

Databricks Unity Catalog

Databricks Repos

AWS Lambda

S3

Step Functions

Terraform

CloudFormation

Description du poste

Senior Data Engineer (Data Engineer III) - AWS & Databricks Focus

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.


• 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

Preferred Qualifications

• 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.