Devops Engineer (Data Services & Linux) - Remote - Latin America
Fullstack
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Descripción de la vacante
Chevron Global Business Services (GBS) in Buenos Aires, Argentina, is seeking a Data Engineer to design and operate scalable ETL/ELT pipelines using Databricks, PySpark, and SQL. You will enable data scientists by making data accessible and building data products aligned with architecture.
You will collaborate with ML engineers, scale and productionize workflows, ensure data quality, and optimize performance across Delta Lake, BigQuery, and cloud services, joining a global IT organization
Formación
- 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; analytics solution architecture.
- Knowledge of big data computing and cloud computing.
- Experience with ETL/ELT, data pipelines, data quality and security.
Responsabilidades
- Understand business use of data and stakeholder requirements to support work processes and strategic objectives.
- Leverage data, software engineering and data science techniques to create business value through data accessibility (ingestion, preparation, analytics).
- 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 and contribute to models.
- Collaborate with ML engineers to scale and deploy data solutions.
Conocimientos
Educación
Herramientas
Descripción del empleo
Buenos Aires, Buenos Aires, Argentina
Full time
R
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 Description
**GBS 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 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.
Required Qualifications:
+ BS in Computer Science, Management Information Systems, Computer Engineer or related fields or equivalent experience.
+ +5 years in Data Engineer Role
+ 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
+ Analytical thinking, Consulting, Critical Thinking
**Relocation Options:**
Relocation **may be** considered.
**International Considerations:**
Expatriate assignments **will not be** considered
**Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this position**
Chevron participates in E-Verify in certain locations as required by law.
Chevron Corporation is one of the world's leading integrated energy companies. Through its subsidiaries that conduct business worldwide, the company is involved in virtually every facet of the energy industry. Chevron explores for, produces and transports crude oil and natural gas; refines, markets and distributes transportation fuels and lubricants; manufactures and sells petrochemicals and additives; generates power; and develops and deploys technologies that enhance business value in every aspect of the company's operations. Chevron is based in Houston, Texas. More information about Chevron is available at .
Chevron is an Equal Opportunity / Aff... (truncated) ...
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
We are looking for a Data Engineer with strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.
- Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
- Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
- Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
- Develop data pipelines and processing solutions to support new business requirements and datasets.
- Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.
- Implement data quality checks, monitoring, validation, exception handling, and production controls.
- Optimize PySpark and SQL workloads for performance, reliability, and scalability.
- Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.
- Troubleshoot complex data and production issues and implement sustainable solutions.
- Collaborate with business, data engineering, and platform teams to identify further automation opportunities.
- 5+ years of strong hands-on experience with Databricks and PySpark .
- Advanced SQL and data-processing skills .
- Hands-on experience with GCP, particularly BigQuery .
- Experience with Delta Lake and modern data lake/lakehouse architectures .
- Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.
- Experience building reliable, scalable, production-grade data solutions.
- Strong analytical and troubleshooting skills.
- Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.
- Experience developing or integrating AI/agentic workflows, AI agents, or workflow automation solutions.
- Experience applying AI to automate data engineering, validation, troubleshooting, or operational processes.
- Familiarity with orchestration and automation frameworks.
- Experience developing reusable data engineering frameworks and platform components.
- Exposure to productionizing AI-enabled solutions with appropriate validation, monitoring, and human oversight.
- Professional growth : Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation : We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
- A selection of exciting projects : Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
- Flextime : Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.
AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
We are looking for a Data Engineer with strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.
- Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
- Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
- Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
- Develop data pipelines and processing solutions to support new business requirements and datasets.
- Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.
- Implement data quality checks, monitoring, validation, exception handling, and production controls.
- Optimize PySpark and SQL workloads for performance, reliability, and scalability.
- Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.
- Troubleshoot complex data and production issues and implement sustainable solutions.
- Collaborate with business, data engineering, and platform teams to identify further automation opportunities.
- 5+ years of strong hands-on experience with Databricks and PySpark .
- Advanced SQL and data-processing skills .
- Hands-on experience with GCP, particularly BigQuery .
- Experience with Delta Lake and modern data lake/lakehouse architectures .
- Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.
- Experience building reliable, scalable, production-grade data solutions.
- Strong analytical and troubleshooting skills.
- Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.
- Experience developing or integrating AI/agentic workflows, AI agents, or workflow automation solutions.
- Experience applying AI to automate data engineering, validation, troubleshooting, or operational processes.
- Familiarity with orchestration and automation frameworks.
- Experience developing reusable data engineering frameworks and platform components.
- Exposure to productionizing AI-enabled solutions with appropriate validation, monitoring, and human oversight.
- Professional growth : Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation : We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
- A selection of exciting projects : Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
- Flextime : Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.
Strong hands-on experience with Databricks and PySpark. Advanced SQL and data-processing skills. Hands-on experience with Google Cloud Platform (GCP), particularly BigQuery. Experience with Delta Lake and modern data lake / lakehouse architectures. Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality. Experience building reliable, scalable, production-grade data solutions. Strong analytical and troubleshooting skills. Understanding of software engineering practices including testing, version control, deployment, monitoring, and production support.
AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
We are looking for a Senior/Lead Data Engineer to build and maintain the pipelines and data models behind a centralized HR data warehouse. Working in Snowflake, DBT, Python, and Airflow, this person designs semantic layers that make HR data consumable by both BI tools and AI agents. Experience with Snowflake Cortex or MCP-based tooling for agentic querying is a strong plus.
- Develop and maintain secure, efficient data pipelines using DBT, PySpark, and Python applications within a supported cloud environment for HR data sources.
- Build the infrastructure and tooling required for optimal extraction, transformation, and loading of data from a wide variety of sources using Python, DBT, Terraform, and AWS technologies such as Glue, EMR, and S3.
- Identify and implement internal process improvements, develop tests within the Data Quality Framework, monitor pipelines to maintain 99.5% uptime, and create and maintain the data models used by partner teams.
- Design and build semantic views and ontology layers on top of existing dbt models (CORE → SEMANTIC → METRICS) so HR data is consumable by both BI tools and AI agents/LLMs, defining business-friendly entities, relationships, and metrics that abstract away raw warehouse structure.
- Develop and deploy AI agents (e.g., via Snowflake Cortex Analyst/Cortex Search) for HR COE use cases — including PE, iCIMS, LMS, and Sales/CS Worker agents — enabling natural-language querying over certified HRDM data.
- Configure and maintain MCP (Model Context Protocol) connections between Snowflake and internal AI tooling to securely expose HR data to conversational and agentic interfaces.
- 4+ years of experience .
- Snowflake , including data modeling, datamarts, and data warehouse design.
- DBT for data transformations.
- Python , including object-oriented programming and data scripting.
- Airflow for pipeline orchestration.
- REST API integration and data ingestion .
- Google BigQuery querying and optimization.
- Secure handling of large-scale and sensitive data.
- Real-time data processing (e.g., from Google Sheets or APIs).
- Strong SQL , including highly optimized queries.
- Comprehensive documentation skills.
- Upper-intermediate English level.
- Semantic layer / semantic modeling design, providing business-object abstraction over dbt and warehouse models.
- Snowflake Cortex (Cortex Analyst, Cortex Search) or equivalent LLM-native query layers.
- MCP (Model Context Protocol) or similar tool-calling / context-exposure frameworks.
- Familiarity with prompt and context engineering for grounding LLM agents in certified data sources.
- Professional growth : Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation : We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
- A selection of exciting projects : Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
- Flextime : Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.
+ 4 years of experience. Snowflake, including data modeling, datamarts, and data warehouse design. DBT for data transformations. Python, including object-oriented programming and data scripting. Airflow for pipeline orchestration. REST API integration and data ingestion. Google BigQuery querying and optimization. Secure handling of large-scale and sensitive data. Real-time data processing (e.g., from Google Sheets or APIs). Strong SQL, including highly optimized queries. Comprehensive documentation skills.
LangChain.
LangGraph.
AWS.
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