Senior Cloud Data Ingest Engineer | Pipelines & Ingestion

BeaconAI; Quilmes; Remote; ARS 1,000,000 - 1,600,000; Full time

Job summary

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to join a multinational IT organization delivering data solutions. The role focuses on building and maintaining automated data pipelines using Azure data services, supporting data architecture and analytics across the enterprise.

Responsibilities include data ingestion, processing, and normalization from diverse sources, enabling data scientists with accessible datasets and collaborating with ML

Qualifications

• BS in Computer Science or related field.
• 5+ years in a Data Engineer role.
• Experience with Azure data services (Data Factory, Databricks, Synapse).
• Strong scripting in Python/Java and CI/CD tooling.

Responsibilities

• Understand business data needs and stakeholder requirements to support processes and objectives.
• Apply data engineering, data science, and analytics to create business value and enable data accessibility.
• Ingest, prepare, and transform data; develop data products aligned with architecture patterns.
• Normalize and ingest data from multiple sources.
• Collaborate with ML team to scale and deploy models and document processes.
• Contribute to foundational tools and deployment of technical services.

Skills

Analytical thinking

Consulting

Critical thinking

Education

BS in Computer Science

Related fields acceptable

Tools

Azure Data Factory

Databricks

Python

Java

Azure SQL

Azure Synapse

GIT

Azure Data Lake

Azure Analysis Services

Logic Apps

Microsoft Data Flows

Kimball Data Warehousing

Job description

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to join a multinational IT organization delivering data solutions. The role focuses on building and maintaining automated data pipelines using Azure data services, supporting data architecture and analytics across the enterprise.

Responsibilities include data ingestion, processing, and normalization from diverse sources, enabling data scientists with accessible datasets and collaborating with ML