Senior .Net & Vue Engineer — Cloud, Big Data & Etl
Fullstack
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Descripción de la vacante
Chevron Global Business Services (GBS) in Buenos Aires, Argentina, seeks a Data Engineer to design and maintain scalable data pipelines using Databricks, PySpark, and SQL. You will work within a multinational IT organization, delivering automated data transformations and enabling advanced analytics across cloud platforms.
Ideal candidates have 5+ years of data engineering experience, strong SQL, and hands-on Databricks/PySpark, with English proficiency to operate as a working language.
Formación
- BS in Computer Science, MIS, Computer Engineering or related field; equivalent experience.
- 5+ years as Data Engineer or similar role.
- Strong hands-on experience with Databricks and PySpark; SQL proficiency.
Responsabilidades
- Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
- Build and operationalize agentic workflows to automate data engineering processes.
- Collaborate with platform teams to deploy data pipelines in cloud environments (GCP/AWS).
- Ensure data quality, monitoring, and production controls across data pipelines.
- Support data analysts and data scientists by providing reliable data sources and transformations.
Conocimientos
Educación
Herramientas
Descripción del empleo
Responsible for the daily operation, configuration, monitoring, and maintenance of the cloud BigData service field in the Latin American region, and serves as the second-line support for handling demand changes, customer incident, and alarm tickets.
Responsible for the active operation and maintenance of a single cloud service, including SLO construction, process optimization, capacity management, standardized transformation, disaster recovery construction, daily drills, etc., by developing corresponding automation plug‑ins on the tool platform to optimize operation and maintenance efficiency and availability.
Understand the core design principles of Hadoop, including HDFS architecture and read/write flows, YARN's resource scheduling model, and the MapReduce computing paradigm.
Understand basic data warehouse concepts (, fact tables, dimension tables) and the architecture of Hive, especially the role of the Metastore and how HQL is translated into computation jobs.
Understand the core concepts of Spark, such as RDD resilience and fault tolerance, the advantages of DataFrames for structured data processing, and the execution flow of a Spark job.
Strong knowledge of Linux operating systems and environments.
A self‑starter able to work independently but comfortable working in a team environment.
Ability to quickly learn new or unfamiliar technology and products using documentation and internet resources.
General skills of the operating system: Familiar with the daily operation and maintenance of the operating system, master the troubleshooting and handling of common operating system problems, experience in using shell/python for automated operation and maintenance is preferred.
General troubleshooting skills: System Log analysis, Packages catch and Analysis, Traffic analysis, Monitor design and optimize.
Ability to write common HQL/SQL for data querying, joins, and simple aggregations.
Possess basic troubleshooting skills to identify the preliminary cause of MapReduce/Spark job failures (, OOM, data skew) by analyzing YARN UI and log files.
Proficient in English and can use it as a working language (Mandatory)
+1 Year of experience.
Must be available to work On‑Site.
AWS/Azure/Google Cloud Certification or other related certificates.
Buenos Aires, Buenos Aires, Argentina
Full time
R
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 centre 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.
- 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.
- 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 may be considered.
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 is an Equal Opportunity / Aff‑irmative Action employer. Qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status, or other status protected by law or regulation.
Chevron participates in E-Verify in certain locations as required by law.
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 productionise agentic workflows for data engineering automation.
- Design, develop, and optimise scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
- Build and operationalise 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.
- Optimise PySpark and SQL workloads for performance, reliability and scalability.
- Support testing, deployment, productionisation 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 optimisation, 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 productionising AI-enabled solutions with appropriate validation, monitoring, and human oversight.
- Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalised 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 optimisation, 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.
We are looking for a Senior/Lead Data Engineer to build and maintain the pipelines and data models behind a centralised 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 modelling, 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 optimisation.
- Secure handling of large‑scale and sensitive data.
- Real-time data processing (e.g., from Google Sheets or APIs).
- Strong SQL, including highly optimised queries.
- Semantic layer / semantic modelling 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 personalised 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.
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