Experience in Serverless Solutions and Azure Integration
Deep experience building solutions with
Serverless
and
Azure Integration
, including
Functions
and
Logic Apps
for event‐driven systems.
Azure Functions
Hands‐on expertise in developing serverless computing solutions.
Working with
triggers
(HTTP, Timer, Queue, Event Grid).
Utilizing
bindings
,
function apps
, scaling strategies, and local development using
Azure Functions Core Tools
.
Azure Logic Apps
Extensive experience with
iPaaS
(Integration Platform as a Service).
Building workflows via the
visual designer
.
Leveraging
pre-built connectors
and integrating diverse applications and data sources.
Managing
JSON schema definitions
for triggers and configuring
retry policies
.
Connecting Serverless Components
Strong ability to connect serverless components using
Azure Service Bus
(for message queuing) and
Event Grid
(for event routing), building resilient and decoupled architectures.
Azure API Management
Designing, publishing, and securing APIs, especially those backed by serverless backend services.
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 **** 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 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 / Affimative 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.
**** fastest growing private companies in America and in **** we were #13 in Denver Business Journals list of top places to work.
We are a product company
We are dedicated to "creating a safe place for life"
We build software
We are a B2B Software and
6
LangChain.
LangGraph.
AWS.
We have an exciting and rewarding opportunity for you to take your data engineering career to the next level.
Job responsibilities
Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Collaborates with software engineers and teams to design, develop, test, and implement availability, reliability, scalability, and solutions in their applications
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems




Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way.
Supports review of controls to ensure sufficient protection of enterprise data
Contributes to data and software engineering communities of practice and events that explore new and emerging technologies
Required qualifications, capabilities, and skills
Formal training or relevant data engineering certification and 5+ years applied experience
Experience in Qlik Sense in both ETL and Dashboard development
In depth knowledge of Qliksense scripting, QVD Files, Set Analysis
Practical experience in full stack projects, hands‐on mainly with backend technologies such Java, Spring Boot, JPA (Java Persistence API) Spring Data Repositories
Experience in developing, debugging, and maintaining code in a corporate ecosystem with one or more modern programming languages and database querying languages
Exposure to agile methodologies such as CI/CD, Application Resiliency, and Cybersecurity best practices
Experience with SQL relational DB technologies such as MySQL, Oracle RDMS.
Preferred qualifications, capabilities, and skills
Knowledge of Web Frameworks (. React) will be an added advantage.
Exposure to public cloud technologies, AWS/GCP/Azure experience or certifications is a plus
Experience in observability, monitoring, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others.
Familiarity with industry-wide technology trends and best practices
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 : Versátil schedule with remote and office options.
#J-*****-Ljbffr

📌 Staff Backend Engineer — Data Pipelines & Cloud (Quilmes)
🏢 Proofpoint
📍 Quilmes