Lead Data Ingestion Engineer — Global Pipelines

Bluelight; Buenos Aires; On-site; ARS 1,200,000 - 2,400,000; Full time

Job summary

Chevron Global Business Services (GBS) in Buenos Aires invites an experienced Data Engineer to join the IT Organization. You will design and maintain automated data transformation pipelines leveraging Azure Data Factory, Databricks, and various data storage technologies to support data architecture design.

Responsibilities include data ingestion, preparation, analytics processing, and enabling data scientists with accessible data for advanced models.

Qualifications

• BS in Computer Science, MIS, Computer Engineering or related field.
• +5 years in Data Engineer role.
• Experience with ETL/ELT, data movement, transformation, and analytics architecture.
• Experience with Azure-based data services and cloud computing.

Responsibilities

• Understand business data needs and stakeholder requirements.
• Leverage data, software engineering, and data science techniques to create value via data access.
• Ingest, prepare, and transform data; normalize and store from multiple sources.
• Enable data scientists by providing accessible data and modeling support.
• Collaborate with ML Engineer to scale and deploy models.
• Contribute to inner-source tools and deploy services.

Skills

Analytical thinking

Consulting

Critical thinking

Data integration

Data modeling

Education

Bachelor's degree in Computer Science, MIS, Computer Engineering or related field

Tools

Azure Data Factory

Databricks

DevOps

CI/CD Pipeline Deployment

Python

Java

Ansible

Azure SQL

Azure Synapse

Git

ADO

Azure Data Lake

Logic Apps

Microsoft Data Flows

Kimball Data Warehousing Methodology

Job description

Chevron Global Business Services (GBS) in Buenos Aires invites an experienced Data Engineer to join the IT Organization. You will design and maintain automated data transformation pipelines leveraging Azure Data Factory, Databricks, and various data storage technologies to support data architecture design.

Responsibilities include data ingestion, preparation, analytics processing, and enabling data scientists with accessible data for advanced models.