Senior Remote Data Engineer — Marketing Analytics & Pipelines

BairesDev; Quilmes; Hybrid; ARS 139,474,000 - 201,463,000; Full time

Benefits offered by this job

Professional growth

USD-based compensation

Exciting projects

Flextime

Job summary

Chevron Global Business Services (GBS) in Buenos Aires is seeking a Mid‑level Data Engineer to build dbt pipelines and Snowflake data marts behind customer reporting, spend analytics, and AI features.

You will translate complex procurement data requirements into robust, production‑grade pipelines, optimize performance, and collaborate with ML, analytics, and software teams across the organization.

Qualifications

• 3+ years in analytics/data engineering or a closely related role
• Solid dbt experience: modeling, tests, macros, and incremental models
• Strong SQL and Python programming skills
• Experience with modern cloud data warehouses and ingestion/orchestration tools

Responsibilities

• Build and maintain dbt pipelines, data marts, and warehouse models for reporting, spend analytics, and AI features.
• Translate procurement business logic into maintainable, well-tested pipeline code.
• Contribute to semantic layer and data governance to ensure consistent metrics across teams.
• Maintain data quality, lineage, and documentation for end-to-end reliability.

Skills

dbt experience

SQL

Python

Analytical thinking

Data engineering

Education

Bachelor's degree in Computer Science / related field

Tools

Airbyte

Prefect

Snowflake

Job description

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...

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.

The mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper‑intermediate English level.

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 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.

ABOUT THE ROLE

We are looking for a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling.

WHAT YOU WILL DO

• Develop and implement tooling and datasets that drive telemetry‑driven cost attribution and performance‑informed financial modeling, enabling product, engineering, analyst, and finance stakeholders to make faster, data‑driven decisions and trust the data.
• Build and maintain data pipelines and tooling that provide cost transparency, surface optimization opportunities, and support committed use discount modeling and management.

MUST HAVES

• 4+ years of experience in Data Engineering roles.
• Hands‑on experience with SQL, Python, Spark, and Scala.
• Hands‑on experience with workflow orchestration tools such as dbt, Airflow, and Dagster.
• Hands‑on experience with AWS (Glue, Aurora, Athena), GCP, and Snowflake.
• Experience architecting and enhancing the long‑term maintainability of end‑to‑end ELT/ETL data pipelines, including data modeling and schema maturity.
• Exposure to CI/CD development processes, Infrastructure as Code (Terraform), and REST API programming.
• Strong attention to detail, analytical skill, and comprehensive data auditing and end‑to‑end validation skills.
• Ability to explain technical concepts to non‑technical audiences.

NICE TO HAVES

• Experience with FinOps teams or cost optimization job duties.
• FinOps Practitioner or FinOps Engineer certification.
• Experience with Datadog API endpoints.
• Experience with Go.

PERKS AND BENEFITS

• 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.

Argentina Full-time