AI Engineer / IA Data Engineer 21 applicants
Design, develop, and implement scalable Generative AI solutions using Python and LLMs. Manage cloud infrastructure as code and integrate AI components into productive environments.
100 Data Engineer jobs in Mexico available for remote work from home. Apply for positions such as AI Engineer / IA Data Engineer, Senior Data Engineer (R13923), Staff Software Engineer - Data Processing & Execution Platform and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design, develop, and implement scalable Generative AI solutions using Python and LLMs. Manage cloud infrastructure as code and integrate AI components into productive environments.
You will lead the design and implementation of scalable data architectures and develop robust ETL pipelines to support business analytical needs. Additionally, you will provide technical leadership, mentor junior engineers, and collaborate with cross-functional teams to ensure high-quality software delivery.
You will serve as the technical lead for the data processing and execution platform, defining system architecture and migrating key components to a Node.js/TypeScript and Python ecosystem. You will also architect asynchronous data pipelines and ensure the scalability, security, and reliability of services running on Kubernetes and AWS.
You will lead the development and optimization of efficient data pipelines between Snowflake and Google Cloud Platform. Responsibilities include implementing native orchestration, config-driven processing, and ensuring robust data governance and observability.
Design, develop, and maintain automated ETL pipelines using Python and Apache Airflow. Optimize SQL queries for data warehouse performance and ensure data quality through rigorous testing and documentation.
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You will design, build, and deploy complex data solutions to support AI, machine learning, and business intelligence initiatives. Additionally, you will collaborate with customers to provide technical guidance and ensure the quality and security of data pipelines.
Design, build, and deploy scalable data solutions and pipelines to support AI, ML, and BI insights for customers. Collaborate with internal teams and clients to operationalize complex data transformations and ensure data quality.
The role involves designing and implementing standardized ETL pipelines and data models to migrate data from over 80 ERP systems into Microsoft Fabric. The Lead will establish data quality frameworks and mentor team members to ensure a unified, AI-ready data architecture.
Lead the design and implementation of standardized ETL pipelines and data transformation processes to migrate ERP data into Microsoft Fabric. Establish data quality frameworks and mentor team members on engineering practices to create an AI-ready data architecture.
Lead the implementation of a scalable data quality and observability framework using GX Core to detect and prevent data issues. Establish validation standards, maintain data lineage, and integrate quality checks into CI/CD pipelines to improve business confidence.
Design, build, and maintain scalable data pipelines and ETL processes using Databricks and Apache Spark. Implement Lakehouse architecture and collaborate with data scientists to enable advanced analytics and machine learning workloads.
Design and build scalable batch and real-time data pipelines and lakehouse solutions to support a growing customer base. Collaborate with Data Science and DevOps teams to productionize ML/AI workloads and ensure robust data flows.
Design and maintain scalable ETL/ELT data pipelines and orchestration workflows using Python and DBT. Optimize the Elastic Hierarchy data framework and support data operations across SaaS and on-premise hybrid environments.
Design, implement, and optimize modern data architectures using Big Data technologies, specifically focusing on data lakes and lakehouses. You will be responsible for collecting, cleaning, and orchestrating large volumes of structured and semi-structured data to drive business decision-making.
Design, develop, and maintain scalable ETL/ELT pipelines and data integration solutions using BigQuery and Python. Collaborate with Analytics and BI teams to optimize SQL queries and ensure data quality and availability.
Design, build, and maintain scalable production-grade data systems and resilient pipelines to support analytics and machine learning. Collaborate with cross-functional teams to translate business needs into technical solutions while mentoring other engineers.
The role involves managing the Databricks environment, implementing data governance via Unity Catalog, and overseeing data warehousing operations. You will be responsible for end-to-end maintenance of data pipelines and ensuring reliable data delivery across multiple cloud platforms.
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The Senior Data Engineer will collaborate with cross-functional teams to migrate data from Snowflake on AWS to Azure Databricks and DBT. This role includes developing and maintaining infrastructure-as-code and supporting data transformation processes.
Design, build, and optimize scalable and secure data platforms using AWS services like Glue, EMR, and Lake Formation. Develop efficient data pipelines, implement modern data architectures, and automate infrastructure using Terraform.
Design and develop data pipelines using Azure Data Factory and manage complex data migrations into Microsoft Dynamics 365. Perform data staging, transformation, and validation while ensuring seamless integration between source systems and the target environment.
Design, build, and maintain data models, dashboards, and reports to support cross-functional programs and projects. Bridge the gap between business and technical teams while managing large datasets to serve as a source of truth for reporting and AI.
You will lead the development and evolution of the Human Data platform, including evaluation environments for RL, coding, and STEM. Additionally, you will manage operational workflows such as payments, task approvals, and project taxonomies to ensure production efficiency.
The Senior Solutions Engineer will partner with sales and leadership to design and scale complex AI data and model evaluation workflows for enterprise clients. They are responsible for translating ambiguous customer requirements into actionable pilot plans, proposals, and technical solution designs.
The Senior Data Platform Engineer will design, build, and maintain modern lakehouse platforms, specifically focusing on Microsoft Fabric and OneLake. They are responsible for ensuring data quality, reliability, and performance throughout the full data lifecycle from ingestion to production support.
You will build and maintain scalable, secure data pipelines and platform components using AWS and Snowflake. Additionally, you will integrate AI capabilities into data systems and collaborate with cross-functional teams to deliver trustworthy, high-performance data solutions.
The role involves building production-grade agentic workflows and LLM-powered systems while managing data pipelines and semantic context layers. You will also be responsible for establishing evaluation practices, observability, and providing technical leadership to clients.
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Design, develop, and manage enterprise data integration pipelines using Informatica IDMC to feed Databricks Lakehouse. Optimize ETL/ELT processes and ensure scalability, performance, and compliance with established SLAs.
Own and maintain the semantic layer and data models to serve as the single source of truth for humans and AI agents. Architect the data platform for AI-readiness while driving end-to-end data quality and enabling self-serve analytics.
Design and implement a data quality framework across Bronze, Silver, and Gold layers while maintaining automated checks within Databricks pipelines. Collaborate with Data Engineering and business teams to validate data pipelines, perform reconciliation, and ensure production-ready data assets.
The role involves serving as a trusted advisor for data platforms and AI solutions while delivering technical workshops and customer enablement activities. You will also assess data architecture health, troubleshoot complex data pipelines, and develop modernization plans to improve performance and reliability.
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