Top Data Engineer Jobs in Mexico

As an ETL Data Engineer, develop and maintain data integration processes using Python and Azure Synapse Analytics, ensuring data accuracy for insights.

The ETL Data Engineer will design and maintain data integration processes using Python and Azure Synapse Analytics, ensuring data quality and collaboration across teams.

The Azure Data Engineer will develop ETL processes, manage data integration using Python and Azure Synapse, ensure data quality, and collaborate on data modeling.

As an ETL Data Engineer, you'll design and maintain data integration processes using Python and Azure Synapse Analytics, collaborating with data teams to ensure data quality and performance optimization.

As an ETL Data Engineer, you will design, develop, and maintain data integration processes using Python and Azure Synapse Analytics, ensuring data accuracy and availability for insights and decision-making.

This role involves designing, developing, and maintaining ETL processes using Python and Azure Synapse Analytics, ensuring data accuracy, collaboration, and data integration from various sources.

As an ETL Data Engineer, develop and maintain ETL processes using Python and Azure Synapse Analytics, optimizing data integration and ensuring quality for analytics.

The Data Architect role involves defining the data strategy, optimizing data schemas, enhancing performance, and collaborating with multiple teams for effective data management.

The Data Engineer I will design and manage data pipelines, integrate ETL processes, ensure data quality, and optimize performance while working with various cloud and on-premises technologies.

The Azure Data Engineer will participate in the software development lifecycle, including design, coding, testing, and documentation of applications, collaborating closely with team members and following best practices.

Lead AWS Data Engineer responsible for designing and implementing cloud-native data solutions, overseeing data migrations, and mentoring engineering teams.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, deploy, and operate scalable cloud-based data and analytics solutions. Integrate AI services (Azure OpenAI, Cognitive Services), implement monitoring, collaborate cross-functionally, and apply engineering best practices across end-to-end systems.

Support and improve enterprise data platform stability and performance using Azure technologies. Build, integrate, transform, and deliver data solutions, perform root cause analysis, resolve incidents to meet SLAs, collaborate with stakeholders, and apply DevOps practices and governance to ensure scalable, production-ready data services.

Design, build, and maintain ETL pipelines, dashboards, and corporate data architecture for Sales, Finance, HR, and software teams. Improve data quality, automate processes, create AI agents, port dashboards to new architectures, and collaborate with engineering to scale data solutions.

Build, maintain, and optimize scalable ETL/ELT data pipelines and data solutions to ensure data quality, reliability, and performance. Support data ingestion, transformation, delivery, modeling (star schema/dimensional), debugging, and pipeline performance improvements while collaborating with global stakeholders.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

As a Data Engineer, you'll design and build data ingestion frameworks and manage data pipelines for advanced automotive analytics, collaborating with cross-functional teams.

Build and own core data pipelines and models for Lyft's Safety and Customer Care platform. Scale data processing, tune SQL/MapReduce jobs, implement data quality systems, develop ETL self-service tools, and collaborate cross-functionally to deliver accurate reporting and analytics.

Design, build, and maintain Databricks data pipelines using PySpark, SQL, and Delta Lake. Implement medallion architecture, ensure data quality and lineage, optimize performance, troubleshoot production issues, document pipelines, and collaborate with stakeholders. Support version control, testing, deployment, and improvements; assist with migrating Alteryx workflows and building Power BI visualizations.

Lead design, development, and optimization of cloud-native data platforms and ETL/ELT pipelines. Architect data models, enforce engineering best practices, implement orchestration (Airflow/dbt), monitoring, and data quality frameworks. Mentor engineers, drive architectural decisions, ensure security and governance, and support analytics and machine learning initiatives across teams.

Design and build multi-tenant backend and data foundations: PostgreSQL schemas with RLS, async Python services (FastAPI), OpenAPI-first contracts, and orchestrated data pipelines for secure, scalable AI platforms.

Design, build, and maintain data pipelines and ETL/ELT processes to transform raw data into business-ready datasets. Monitor data quality, integrate data via APIs and cloud platforms, support lightweight data applications, and collaborate with business stakeholders on governance, security, and automation improvements.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture; integrate multiple data sources; ensure data quality, governance, security, and performance; monitor pipelines; support BI, analytics, and ML; document models and workflows; collaborate with stakeholders.