Geospatial Data Engineer — Build ML-Ready Data Pipelines

Accelone Llc; Partido de Quilmes; Presencial; ARS 91.155.000 - 136.732.000; Jornada completa

Descripción de la vacante

GBS Chevron Global Business Services (GBS) in Buenos Aires, Argentina is seeking a Data Engineer to design, build, and maintain automated data pipelines and data products. You will work within a global IT organization delivering data solutions to the corporation.

Responsibilities include ingesting, preparing, validating data, enabling data scientists, and collaborating with ML engineers to scale models. A strong foundation in cloud data architectures and ETL tooling is essential.

Formación

• BS in Computer Science or related field or equivalent experience.
• +5 years in Data Engineer role.
• Experience with Azure Data Factory, Databricks and cloud data architectures.
• Strong data acquisition, preparation, validation and transformation skills.
• Analytical thinking and problem solving required.

Responsabilidades

• Understand business use of data and stakeholder requirements.
• Leverage data, software engineering, and data science techniques to create business value.
• Identify, acquire, cleanse & prepare, store data and develop data products.
• Normalize and ingest data from multiple sources.
• Enable data scientists, making data available for analytics models.
• Work with ML Engineer to scale and deploy solutions.
• Contribute to internal tooling and services deployment.

Conocimientos

Big data

Cloud computing

Analytical thinking

Critical thinking

Educación

BS in CS or related

Herramientas

Azure Data Factory

Databricks

Python

Java

Ansible

Azure SQL

Azure Synapse

Git

ADO

Azure Data Lake

Logic Apps

Descripción del empleo

GBS Chevron Global Business Services (GBS) in Buenos Aires, Argentina is seeking a Data Engineer to design, build, and maintain automated data pipelines and data products. You will work within a global IT organization delivering data solutions to the corporation.

Responsibilities include ingesting, preparing, validating data, enabling data scientists, and collaborating with ML engineers to scale models. A strong foundation in cloud data architectures and ETL tooling is essential.