66 Data Engineer jobs in Argentina available for remote work from home. Apply for positions such as Data Engineer, Data Engineer, Data Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will build and optimize ETL data pipelines while designing scalable ingestion systems for large-scale datasets. Additionally, you will architect cloud-based infrastructure and collaborate with data scientists to transform raw data into actionable business insights.
Design and optimize data pipelines using Airflow and AWS to load data into Snowflake. Build modular dbt models and maintain the orchestration layer to ensure high-quality data delivery for analytics.
Maintain, troubleshoot, and optimize a data ingestion pipeline using Java and Python. Collaborate with data scientists and engineers to improve observability and ensure high-quality data flow.
Build and maintain scalable, reliable data pipelines and govern performant cloud-deployed relational and non-relational databases. Collaborate on secure architecture definitions to drive data systems toward near real-time performance.
Design and maintain high-throughput batch and streaming data pipelines to process global financial transactions. Collaborate with cross-functional teams to build scalable data warehouse and lakehouse architectures while enforcing data quality and governance standards.
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You will own the data pipeline and analytical architecture for a large-volume marketing analytics platform, making key decisions on partitioning, schema design, and near-real-time processing. Additionally, you will lead a team of senior developers, enforce code quality standards, and drive the AWS data stack while managing complex, underspecified technical problems.
You will build and maintain data pipelines and GTM data assets across Snowflake, Salesforce Data Cloud, and GCP. Additionally, you will collaborate with cross-functional teams to optimize SQL queries and ensure data quality for revenue analytics.
You will build and maintain scalable data pipelines and integration layers to support enterprise analytics and Agentic AI ecosystems. Additionally, you will collaborate with cross-functional teams to implement data quality, observability, and security standards across production systems.
The role involves co-developing batch and streaming pipelines on Databricks while defining data contracts and quality rules. You will also mentor domain teams on data modeling and engineering best practices to improve platform templates.
The role involves managing data collection and ingestion pipelines to support AI model training operations. You will collaborate with scientists to optimize infrastructure for high-quality, large-scale data processing.
The role involves managing data collection and ingestion pipelines to support large-scale AI model training. You will collaborate with scientists and leadership to optimize data infrastructure and define the dataset roadmap for future products.
Design, develop, and implement scalable Generative AI solutions using Python and LLMs. Manage cloud infrastructure as code and integrate AI components into productive environments.
Design and implement a scalable data quality framework while operationalizing GX Core for data validation. Develop observability solutions, maintain data lineage, and integrate quality checks into CI/CD pipelines to ensure data integrity.
Design, build, and maintain data pipelines using Python and SQL while managing Azure-based data platforms. Collaborate with stakeholders to provide business-friendly insights and ensure data quality through process improvements.
You will implement new functionality for the core Analytics platform and maintain existing products to ensure high performance and UI responsiveness. You will also collaborate with cross-functional teams to take features from ideation to production while utilizing AI-assisted coding tools.
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You will implement new functionality for the core Analytics platform and maintain existing products to ensure high performance and responsiveness. You will also collaborate with cross-functional teams to take features from ideation to production while raising the technical baseline of the codebase.
You will design, build, and deploy complex data solutions while collaborating with internal teams and customers to support their data journey. Additionally, you will provide technical guidance, ensure data quality, and maintain data governance across multiple customer accounts.
The role involves co-developing batch and streaming pipelines, semantic models, and transformations using Databricks. Additionally, the engineer will define data contracts, mentor domain teams on data modeling, and improve platform tooling based on feedback.
Own the image and video dataset lifecycle, including taxonomy, curation, and quality control. Design and maintain data pipelines and build internal tools to accelerate dataset creation and labeling for the ML team.
Lead the Intelligence strategy and roadmap while building and managing a high-performing AI/ML and data engineering team. Establish the canonical data substrate and AI platform, focusing on RAG architectures, MLOps, and evaluation frameworks.
Build and operate scalable ingestion and transformation pipelines to turn raw fabrication data into AI-ready datasets. Implement medallion-style architectures, vector pipelines for RAG, and real-time CDC flows from MongoDB.
The Senior Data Engineer will design and maintain scalable big data pipelines and commercial products end-to-end. They will collaborate with product managers to define requirements and mentor junior engineers through code reviews and technical guidance.
Design and maintain scalable ETL/ELT pipelines and data storage solutions to process large volumes of diverse data. Develop Tableau dashboards and support AI/ML workflows by building data pipelines for model training and evaluation.
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Design and implement scalable, domain-oriented data pipelines on AWS to power analytics and business-critical workflows. Collaborate with cross-functional teams to optimize batch and streaming ingestion architectures while ensuring data quality and governance.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to optimize data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to ensure data quality, reliability, and performance across various data domains.
Design and build resilient batch and real-time data pipelines to support critical products and business growth. Establish engineering foundations, best practices, and data models to enable scalability and product experimentation.
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