Senior Data Ingestion & Integrations Engineer

Coder Technologies Inc.; Quilmes; Remote; ARS 92,241,000 - 153,735,000; Full time

Job summary

Chevron Global Business Services in Buenos Aires, Argentina, seeks a Data Engineer to design and maintain automated data transformation pipelines using Azure, Databricks and related technologies. You will work with cross-functional teams to support data architecture and analytics across the enterprise.

Responsibilities include data ingestion, preparation, validation, and delivering data products; collaboration with ML engineers; contributing to CI/CD pipelines and internal tooling; relocation

Qualifications

• BS in Computer Science, MIS, or related field or equivalent experience.
• +5 years in Data Engineer role.
• Experience with ETL, data movement, big data computing, cloud data architectures.
• Proficiency with Azure Data Factory, Databricks, Python/Java, CI/CD and related tools.

Responsibilities

• Understand data use and stakeholder requirements to support processes and objectives.
• Leverage data engineering and analytics to create business value through data accessibility (ingestion, prep, analytics).
• Normalize and ingest data from multiple sources and develop data products.
• Enable data scientists by providing accessible data for analytics and ML workflows.
• Collaborate with ML engineers to scale and deploy data solutions.

Skills

Azure Data Factory

Databricks

Python

Java

CI/CD

GIT

Azure Synapse

Azure SQL

Azure Data Lake

Ansible

Education

BS in Computer Science or related field

Tools

Azure Data Factory

Databricks

Azure Synapse

Azure Data Lake

Azure Analysis Services

Git/ADO

Kimball Data Warehousing

Job description

Chevron Global Business Services in Buenos Aires, Argentina, seeks a Data Engineer to design and maintain automated data transformation pipelines using Azure, Databricks and related technologies. You will work with cross-functional teams to support data architecture and analytics across the enterprise.

Responsibilities include data ingestion, preparation, validation, and delivering data products; collaboration with ML engineers; contributing to CI/CD pipelines and internal tooling; relocation