Data Engineer
Chevron
Data Engineer
Job Description
Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.Job DescriptionGBS Chevron Global Business Services (GBS), located in Buenos Aires (Puerto Madero), Argentina, is accepting applications for the position of Data Engineer. Successful candidates will join the IT Organization, which is part of a multifunction service and technical center with a workforce of more than 1800 employees that deliver business services and solutions to the corporation across the globe.Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.Responsibilities include:Understand business use of data and stakeholder requirements to support work processes and strategic business objectivesLeverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patternsResponsible for normalizing and ingesting data from multiple sources.Enable data scientists, making data available for advanced analytics models, and contributing to models.Work with ML Engineer to scale and deploy solution including model, documentation, training, integrationContributing to the inner source development of foundational tools, and/or the deployment of technical services.Required Qualifications:BS in Computer Science, Management Information Systems, Computer Engineer or related fields or equivalent experience.+5 years in Data Engineer RoleKnowledge and/or experience with: data acquisition, preparation and validation; data movement and transformation; analytics solution architecture; big data computing; cloud computing, core data architecture; information security technologies and software engineering using technologies such as: Azure Data Factory (ETL), 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 MethodologyAnalytical thinking, Consulting, Critical ThinkingRelocation Options:Relocation may be considered.International Considerations:Expatriate assignments will not be consideredChevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this positionChevron participates in E-Verify in certain locations as required by law.
Locations
- Argentina, Buenos Aires, Buenos Aires
Required Qualifications
- BS in Computer Science, Management Information Systems, Computer Engineer or related fields or equivalent experience. (experience)
- +5 years in Data Engineer Role (experience, 5 years)
- Knowledge and/or experience with: data acquisition, preparation and validation; data movement and transformation; analytics solution architecture; big data computing; cloud computing, core data architecture; information security technologies and software engineering using technologies such as: Azure Data Factory (ETL), 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 (experience)
- Analytical thinking, Consulting, Critical Thinking (experience)
- BS in Computer Science, Management Information Systems, Computer Engineer or related fields or equivalent experience. (experience)
- +5 years in Data Engineer Role (experience, 5 years)
- Knowledge and/or experience with: data acquisition, preparation and validation; data movement and transformation; analytics solution architecture; big data computing; cloud computing, core data architecture; information security technologies and software engineering using technologies such as: Azure Data Factory (ETL), 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 (experience)
- Analytical thinking, Consulting, Critical Thinking (experience)
Responsibilities
- Understand business use of data and stakeholder requirements to support work processes and strategic business objectives
- Leverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.
- Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns
- Responsible for normalizing and ingesting data from multiple sources.
- Enable data scientists, making data available for advanced analytics models, and contributing to models.
- Work with ML Engineer to scale and deploy solution including model, documentation, training, integration
- Contributing to the inner source development of foundational tools, and/or the deployment of technical services.
- Understand business use of data and stakeholder requirements to support work processes and strategic business objectives
- Leverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.
- Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns
- Responsible for normalizing and ingesting data from multiple sources.
- Enable data scientists, making data available for advanced analytics models, and contributing to models.
- Work with ML Engineer to scale and deploy solution including model, documentation, training, integration
- Contributing to the inner source development of foundational tools, and/or the deployment of technical services.
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