Senior Data Engineer — AWS & Databricks Lead

JPMorganChase

Buenos Aires

Presencial

ARS 2.500.000 - 4.500.000

Jornada completa

14 días+

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Descripción de la vacante

JPMorgan Chase Corporate Sector is seeking a Senior Data Engineer to design and deliver trusted data pipelines in a secure, scalable environment with a focus on AWS and Databricks. You will lead projects, mentor junior engineers, and collaborate with FinOps to optimize cloud costs while ensuring data governance and strong validation practices.

The role emphasizes autonomous project leadership, robust data governance, and the ability to translate business needs into scalable, compliant data

Formación

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

Responsabilidades

Design, develop, and maintain scalable ETL pipelines using AWS services (Lambda, Step Functions, S3) 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.