Remote Azure Data Engineer - Data Pipelines & Analytics

Visa Hunt; Quilmes; Remoto; ARS 1.200.000 - 2.400.000; Tempo pieno

Descrizione del lavoro

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to join its IT organization and support automated data transformation pipelines across diverse storage and compute technologies.

The successful candidate will help ingest, prepare, normalize, and store data from multiple sources, enable data scientists, and collaborate with ML engineers to scale models and deploy data products.

Competenze

• BS in Computer Science, MIS, Computer Engineering or related field, or equivalent experience.
• +5 years in Data Engineer role.
• Experience with data acquisition, preparation, validation; data movement and transformation; analytics solution architecture; big data computing; cloud computing; information security technologies.

Mansioni

• Understand business use of data and stakeholder requirements to support work processes and objectives.
• Leverage data, software engineering, and data science techniques to create business value through data accessibility; includes ingestion, preparation and analytics processing.
• Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns.
• Normalize and ingest data from multiple sources.
• Enable data scientists by making data available for analytics models; contribute to models.
• Collaborate with ML Engineers to scale and deploy solutions; document, train and integrate.

Conoscenze

ETL

Data pipelines

Big data

Python

SQL

Cloud computing

CI/CD

DevOps

Formazione

BS in Computer Science or related field

+5 years Data Engineer experience

Strumenti

Azure Data Factory

Databricks

Azure SQL

Azure Synapse

Git

ADO

Azure Data Lake

Logic Apps

Data Flows

Kimball Data Warehousing

Descrizione del lavoro

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to join its IT organization and support automated data transformation pipelines across diverse storage and compute technologies.

The successful candidate will help ingest, prepare, normalize, and store data from multiple sources, enable data scientists, and collaborate with ML engineers to scale models and deploy data products.