Senior Data Engineer - Dataops & Ingestion (Remote)
Solicitud De Empleo Para Ingeniero De Datos Senior En Encora
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Job summary
Chevron Global Business Services (GBS) in Buenos Aires, Argentina is seeking a Senior Data Engineer to replace a third-party ingestion tool with an owned ETL pipeline. The role involves building scalable data pipelines, enabling data scientists, and ensuring data quality across multiple data sources.
You will work with AWS, Snowflake, DBT, Airflow, and other modern data technologies in a hybrid office/remote setup. Relocation and visa sponsorship are not indicated.
Qualifications
- 4+ years in Data Engineer role or equivalent experience.
- Strong SQL and large-scale data ingestion/pipeline experience (batch ETL, orchestration, schema design).
- Experience with AWS data services (RDS/Postgres, S3, ECS) and cloud environments.
- Hands-on with tools like Databricks, DBT, Airflow, and Terraform; proficient in Python/Scala.
- Familiarity with data ingestion, transformation, and data governance concepts.
Responsibilities
- Document current ingestion logic and data sources.
- Build ETL pipeline to replace legacy system for multiple data sources.
- Evaluate off-the-shelf vs owned ETL solutions with Infra Lead.
- Design standalone owned pipeline powering client dashboards.
- Collaborate with data science and analytics teams to align ingestion with models.
- Operate within AWS environment (ECS, S3, RDS) and contribute to backend tasks.
Skills
Education
Tools
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.
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.
- 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 / Affidative 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.
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 Data Engineer to replace a third‑party data ingestion tool with an owned ETL pipeline.
- 4+ years of strong SQL and large‑scale data ingestion/pipeline experience (batch ETL, orchestration, schema design).
- Working knowledge of AWS data services (RDS/Postgres, S3, ECS) sufficient to build within an existing cloud environment.
- Practical experience with Claude Code or similar tools, and agents or MCP in real work.
- Ability to contribute to application and backend work beyond pure ETL to support broader platform needs.
- Familiarity with, or ability to quickly evaluate, managed ELT tools (such as Fivetran) against custom builds.
- Ability to ramp into existing attribution and scoring logic so ingestion output aligns with downstream dashboard needs.
- Direct or adjacent experience with digital advertising / DSP data (e.g., Amazon DSP, Amazon Advertising API, or comparable programmatic platforms).
- Experience replacing legacy third‑party data pipelines with owned, custom infrastructure.
- Familiarity with MLOps pipelines and how data feeds predictive models.
- Experience working alongside part‑time technical advisory leads to validate architecture decisions.
- Document current ingestion logic inside the legacy tool, including sources, endpoints, schemas, and transformation logic.
- Build the ETL pipeline to replace the legacy system, handling ingestion from the Amazon API, other DSP platforms, and related digital data sources.
- Evaluate and decide between off‑the‑shelf solutions (e.g., Fivetran) and owned ETL systems alongside the Infrastructure Lead.
- Design the pipeline as a standalone, owned system to power client‑facing dashboards.
- Provide flexible engineering support for application and backend work within the AWS environment when directed.
- Coordinate with data science teams to ensure ingestion output aligns with the attribution and scoring logic of downstream models.
- Work seamlessly within the existing AWS environment (ECS, S3, RDS) rather than standing up parallel infrastructure.
- 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.
We are looking for a Data Engineer to build data pipelines and tooling for cost attribution, optimization, and financial modeling.
- 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.
- 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.
- Experience with FinOps teams or cost optimization job duties.
- FinOps Practitioner or FinOps Engineer certification.
- Experience with Datadog API endpoints.
- Experience with Go.
- 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.
- Extract, Transform and Load data from Third Parties into AWS Storage and Databases.
- Building and maintaining AWS Environments.
- Configure monitoring for cloud environments.
- Actively participate in Agile ceremonies and two‑week sprint cycles.
- Experience with AWS Lambda, AWS Neptune, AWS Glue.
- Proficient in SQL for Oracle, No‑SQL DSL for Elasticsearch or any other Database querying language.
- Experience with AWS and Terraform for building and maintaining solutions.
- Cloudwatch or Dynatrace experience.
- Experience working in Agile teams.
- Experience with Python development.
- Experience building and deploying Kubernetes based solutions.
- Experience with Jenkins and Spinnaker pipelines.
- Any experience with Azure cloud provider is also favorable.
- Any experience with ChatGPT API.
- Any experience with ODRL/DCAT.
- Any experience building Business Intelligence solutions.
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.
- 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.
- 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.
- 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.
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