Senior Data Engineer, Marketing Analytics Platform

Agileengine, Llc.; Quilmes; Remoto; ARS 92.724.000 - 154.540.000; Tempo pieno

Descrizione del lavoro

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to join the IT Organization. You will design and deploy automated data transformation pipelines using a range of storage and compute technologies to support data architecture and analytics.

Responsibilities include understanding data use and stakeholder needs, enabling data scientists with accessible data, and collaborating with ML engineers to scale and deploy models and tools.

Competenze

• BS in Computer Science, MIS, Computer Engineering or related field.
• +5 years of Data Engineer experience.
• Experience with data acquisition, preparation and validation; data movement and transformation.

Mansioni

• Understand business data use and stakeholder requirements to support processes.
• Leverage data, software engineering and data science techniques to create value through data accessibility.
• Identify, acquire, cleanse and prepare data; develop data products aligned with defined architecture patterns.
• Normalize and ingest data from multiple sources; enable data scientists with accessible data.
• Collaborate with ML Engineers to scale and deploy solutions and contribute to foundational tools.

Conoscenze

Analytical thinking

Consulting

Critical thinking

Formazione

BS in Computer Science, MIS, Computer Engineer or related fields

Strumenti

Azure Data Factory

Databricks

DevOps

CI/CD Pipeline Deployment

Python

Java

Ansible

Azure SQL

Azure Synapse

GIT

ADO

Azure Data Lake

Azure Analysis Services

Logic Apps

Microsoft Data Flows

Kimball Data Warehousing Methodology

Descrizione del lavoro

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to join the IT Organization. You will design and deploy automated data transformation pipelines using a range of storage and compute technologies to support data architecture and analytics.

Responsibilities include understanding data use and stakeholder needs, enabling data scientists with accessible data, and collaborating with ML engineers to scale and deploy models and tools.