AI-Powered Marketing Data Engineer

Engg; Quilmes; Remoto; ARS 1.200.000 - 2.400.000; Tempo pieno

Descrizione del lavoro

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, seeks a Data Engineer to design and maintain automated data transformation pipelines and support data architecture across diverse storage and compute technologies.

The role involves data ingestion, preparation, and analytics processing, enabling data scientists and ML engineers to build and deploy models within a large multinational IT organization.

Competenze

• BS in Computer Science, MIS, Computer Engineering or related field, or equivalent experience.
• +5 years in Data Engineer role.
• Experience with Azure Data Factory, Databricks, Python, Java, and data warehousing tools.

Mansioni

• Understand business use of data and stakeholder requirements to support work processes and strategic business objectives.
• Leverage data, software engineering, and data science techniques to create business value through data accessibility.
• Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns.
• Responsible for normalizing and ingesting data from multiple sources.
• Enable data scientists, making data available for analytics models and contributing to models.
• Work with ML Engineer to scale and deploy solution including model, documentation, training, integration.
• Contributing to the inner source development of foundational tools and deployment of technical services.

Conoscenze

Analytical thinking

Consulting

Critical thinking

Formazione

BS in Computer Science / MIS / Computer Engineering or related field

Strumenti

Azure Data Factory

Databricks

DevOps

CI/CD

Python

Java

Ansible

Azure SQL

Azure Synapse

Git

ADO

Azure Data Lake

Logic Apps

Microsoft Data Flows

Kimball Data Warehousing

Descrizione del lavoro

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, seeks a Data Engineer to design and maintain automated data transformation pipelines and support data architecture across diverse storage and compute technologies.

The role involves data ingestion, preparation, and analytics processing, enabling data scientists and ML engineers to build and deploy models within a large multinational IT organization.