Senior Data and Artificial Intelligence - Cloud Solution
Partners with customers, sales, engineering and product teams to design, demonstrate and deploy Oracle Cloud architectures that address customer business problems.
Drives Oracle Cloud customer consumption by accelerating the adoption of Oracle cloud services including discovery, design and deployment.
What you'll do
Discover customer's data challenges, priorities, growth expectations, and success criteria.
Design data platforms for integration, storage, processing, and analytics.
Recommend data lake, lakehouse, or data warehouse approaches based on each customer's needs and existing environment.
Design batch and real-time pipelines, including ETL/ELT and integration across platforms.
Help customers make informed architecture trade-offs across security, data governance, performance, scalability, resilience, operational needs, and cost.
Design for reliable operation, including monitoring, failure recovery, and the ability to adapt as demand grows.
Lead technical discussions, demonstrations, and proofs of concept, and help customers bring validated solutions into production.
Work with customer and internal teams to resolve integration and operational challenges and support long-term adoption.
Build the data foundation for analytics, machine learning, and generative AI use cases when relevant.
What we're looking for
8+ years of experience in data engineering, data architecture, or related roles, including hands-on experience designing enterprise data platforms that scale with demand, recover from failures, and operate reliably in production.
Practical knowledge of distributed processing, data integration, and modern analytics architectures.
Experience with technologies such as Spark, Databricks, Snowflake, BigQuery, or cloud data services.
The ability to connect architecture decisions to business outcomes and explain trade-offs to different audiences.
Experience working directly with customers from discovery through delivery.
Experience with OCI and multicloud architectures is a plus.
If you enjoy understanding what customers need, navigating complex data environments, and helping teams put reliable solutions into production, we'd like to hear from you.
Qué harás
Comprender los desafíos de datos de cada cliente, sus prioridades de negocio, planes de crecimiento y criterios de éxito.
Diseñar plataformas para integración, almacenamiento, procesamiento y análisis de datos, eligiendo arquitecturas de data lake, lakehouse o data warehouse según cada caso.
Diseñar pipelines batch y en tiempo real, incluyendo ETL/ELT e integración entre plataformas.
Orientar decisiones de arquitectura considerando seguridad, gobierno de datos, rendimiento, escalabilidad, resiliencia, operación y costos.
Diseñar soluciones preparadas para operar de forma confiable en producción, con monitoreo, recuperación ante fallas y capacidad de adaptación a nuevas demandas.
Liderar conversaciones técnicas, demostraciones y pruebas de concepto, y ayudar a llevar soluciones validadas a producción.
Trabajar con equipos del cliente y de Oracle para resolver desafíos de integración y operación e impulsar la adopción sostenida de Oracle Cloud Infrastructure (OCI).
Establecer la base de datos necesaria para casos de analytics, machine learning e IA generativa cuando corresponda.
Qué buscamos
Más de 8 años de experiencia en ingeniería de datos, arquitectura de datos o funciones relacionadas, incluyendo experiencia práctica en el diseño de plataformas empresariales.
Experiencia con plataformas que escalan, se recuperan de fallas y operan de manera confiable en producción.
Conocimiento práctico de procesamiento distribuido, integración de datos y arquitecturas modernas de analytics.
Experiencia con tecnologías como Spark, Databricks, Snowflake, BigQuery o servicios de datos en la nube.
Capacidad para relacionar decisiones de arquitectura con resultados de negocio y explicar sus ventajas y compromisos a diferentes públicos.
Experiencia trabajando directamente con clientes, desde el descubrimiento de necesidades hasta la entrega de soluciones.
Se valorará la experiencia con OCI y arquitecturas multicloud.
Si te interesa comprender las necesidades de los clientes, trabajar en entornos de datos complejos y ayudar a los equipos a llevar soluciones confiables a producción, nos gustaría conocerte.
Buenos Aires, Buenos Aires, Argentina
Full time
R
Utiliza 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 Integral 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 **** employees that deliver business services and solutions to the corporation across the globe.
**
Utiliza 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 /Affiantive Action employer.
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status, or other status protected by law or regulation.
The Company At Spartan Investment Group our mission is to improve lives through our values.
We do this by finding value-add and opportunistic investments offering solid returns to our investors.
Providing an opportunity to grow for our partners and creating lasting wealth for everyone with whom we conduct business is how we make our greatest contribution.
We specifically focus on self-storage projects capitalizing on amazing deals in other asset classes.
In ****, we were listed as the 5th fastest growing real estate company in the US and joined the list of the top 100 owners of self-storage.
We are members of the Inc.
**** fastest-growing private companies in America and in **** we were #13 in Denver Business Journals list of top places to work.
Mission As the Data Engineer you will design, build, and optimize scalable data infrastructure that powers marketing, business intelligence, and strategic decision-making across the organization.
You will develop reliable data pipelines, integrate third-party platforms, and transform raw data into actionable insights through automation, analytics, and visualization.
Working closely with cross-functional teams, you will ensure data is accurate, accessible, and optimized for performance while continuously identifying opportunities to improve processes and capabilities.
This role combines technical expertise, innovation, and collaboration to deliver high-quality data solutions that support business growth.
Outcomes Data Pipeline Development – Design, build, and optimize end-to-end data pipelines from ingestion through transformation and visualization, delivering scalable, reliable solutions that achieve 99%+ successful pipeline execution.
Platform Integrations – Integrate third-party tools and platforms, including CRMs, MarTech platforms, and custom applications, using APIs, webhooks, and modern integration methods while maintaining reliable data synchronization.
ETL/ELT Optimization – Develop, improve, and maintain ETL/ELT workflows that ensure clean, accurate, and scalable data processing while minimizing failures and manual intervention.
Data Analysis & Insights – Conduct in-depth data and trend analysis to deliver actionable insights that support data-driven marketing strategies and business decision-making.
Cross-Functional Collaboration – Partner with marketing, data, and business intelligence teams to deliver technical solutions that align with business objectives while meeting project timelines and stakeholder expectations.
Data Warehouse Performance – Optimize data storage and retrieval performance across platforms such as BigQuery, Snowflake, or Azure Synapse to improve query efficiency, scalability, and data accessibility.
Business Intelligence Reporting – Create and maintain Power BI and other business intelligence dashboards that provide accurate, timely reporting and actionable insights for stakeholders.
Continuous Improvement & Ownership – Continuously identify opportunities to improve workflows, reporting, and data capabilities while independently managing priorities, communicating progress transparently, and consistently delivering high-quality solutions.
Qualifications 2+ years of experience as a Data Engineer, MarTech Developer, or similar role.
Proficiency in Python and SQL for data engineering, automation, and analytics.
Experience designing, building, and optimizing end-to-end ETL/ELT data pipelines.
Experience developing and maintaining REST APIs, webhooks, and third-party system integrations.
Knowledge of data modeling, data architecture, and data integration best practices.
Experience working with Google BigQuery, Data Lake environments, or similar cloud-based data platforms.
Experience creating reports and dashboards using Power BI, Tableau, or similar business intelligence tools.
Experience with Snowflake, Azure Synapse, or other cloud-based data warehouse platforms preferred.
Experience automating workflows using scripting tools, Zapier, or custom automation solutions preferred.
Experience developing chatbot, Generative AI,



or machine learning solutions preferred.
Experience supporting marketing technology (MarTech), CRM, or business intelligence ecosystems preferred.
Ability to work full-time and maintain significant overlap with U.S.
business hours, including evening or overnight schedules as needed.
Remote Work Requirements Dedicated home office or professional remote work environment.
Reliable high-speed internet connection capable of supporting video conferencing and daily business operations.
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AgileEngine is an Inc. **** 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.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a
Mid-level Data Engineer
to build and maintain dbt pipelines and Snowflake data marts behind customer-facing reporting, spend analytics, and AI features.
WHAT YOU WILL DO
Build and maintain the dbt pipelines, data marts, and warehouse models that serve in-app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
Translate complicated procurement business logic into maintainable, well-tested pipeline code, and notice when a model is valid-looking but wrong.
Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query, so that metrics and dimensions are defined once and answered consistently.
Maintain the boundary between PII and agent/reporting-safe data.
Build and maintain the FastAPI services that expose reporting marts to customers.
Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
Tune Snowflake performance and cost within the models you build.
Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
Share what you learn through code review and team discussion.
Communicate clearly with engineering, product, and customer-facing teams about what the data means and what it can and can't do.
MUST HAVES
3+ years in analytics engineering, data engineering or a closely related role, including hands-on work on production data pipelines that other systems or customers depended on.
Solid dbt experience: modeling, tests, macros, and incremental models.
Strong SQL and working Python.
Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
Experience with a
modern cloud warehouse (Snowflake preferred).
Experience with
ingestion tooling and orchestration (Airbyte, Prefect, or equivalents).
Familiarity with agentic coding tools and an understanding of why guardrails around their output matter.
Deep curiosity about the business: you want to understand what the data means and how customers use it, not just how it flows.
Software engineering discipline for analytics code: Git, code review, CI/CD.
Can take a multi-sprint piece of work from spec to production with normal oversight, and can explain tradeoffs to technical and non-technical stakeholders.
NICE TO HAVES
Experience building or maintaining API services on top of warehouse data (FastAPI or similar).
Experience with a semantic layer or governed metric definitions that multiple consumers rely on, including for LLM-driven or agent-driven querying.
Experience exposing warehouse data to customers through embedded analytics or customer-facing APIs.
Experience with data observability tooling.
Experience supporting customer data onboarding or implementation teams.
Experience deploying data pipelines across multiple regions and managing PII or sensitive data.
Knowledge of procurement and/or supply chain data models.
Prior experience in enterprise software and startups.
PERKS AND BENEFITS
Professional growth
: Mentorship, TechTalks, and personalized growth roadmaps.
Competitive compensation
: USD-based pay with education, fitness, and team activity budgets.
Exciting projects
: Modern solutions with Fortune 500 and top product companies.
Flextime
: Flexible schedule with remote and office options.
LangChain.
LangGraph.
AWS.
6
Argentina Full-time
#J-*****-Ljbffr

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