Top Data Engineer Jobs in Mexico

As a Data Engineer, you will design and build scalable analytics pipelines, manage data integrations, and optimize data architecture for large-scale projects.

The Senior GIS Data Engineer will manage GIS data engineering projects, transforming geospatial data using various technologies, and provide consultancy for data integration. They will also improve processes and collaborate across teams to enhance efficiency and quality.

The Data Engineer is responsible for implementing, optimizing, and monitoring data recovery, storage, and interruption across the organization, requiring advanced data engineering expertise and support in data management and BI reporting.

The Staff Data Engineer will lead data model design, standardize data engineering practices, modernize orchestration, and establish governance strategies within Dropbox's Analytics team.

As a Data Engineer, you will build and maintain data platforms, ensuring high-quality data availability for decision-makers, and develop ETL/ELT processes while utilizing data modeling techniques.

Perform MIS data management and reporting: analyze data, automate reports, ensure data integrity and controls, document reports, assess risks, and support policy recommendations to the team.

Lead design, build, and optimize scalable AWS-based ETL/ELT data pipelines using Python and PySpark. Implement CDC, integrate vendor and customer datasets, ensure data quality and scalability, mentor engineers, participate in architecture and code reviews, and use AI-assisted development tools to improve engineering productivity.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

Design, build, and maintain ETL pipelines and data infrastructure to support analytics and reporting. Write and schedule workflows (Airflow DAGs), manage third-party data ingestion, implement data validation, optimize performance, and collaborate on data models, governance, and ML model deployments.

As a Data Engineer, you will design and implement ETL pipelines, improve data processes, and support business needs through reporting solutions using various technologies and best practices.

The Data Engineer will design and maintain scalable ETL/ELT pipelines on Databricks and AWS, optimizing data infrastructure for gaming analytics and decision-making.

Design, build, and deploy scalable data lakehouse and warehouse platforms; implement and optimize ETL/ELT pipelines (Databricks, dbt); develop data models and Medallion architecture layers; manage CI/CD and Infrastructure as Code (Azure DevOps, Terraform, CloudFormation); use cloud-native serverless offerings across major clouds; ensure data security and governance; integrate pipelines with BI tools like Tableau, Looker, and Power BI; communicate technical designs to stakeholders.

Design, build, and optimize Azure-based data pipelines and storage (data lakes/warehouses). Transform and model large datasets, tune performance, and collaborate with data scientists and stakeholders. Mentor junior engineers and deliver solutions within Agile/Scrum processes.

Design, model, and develop the GCP data ecosystem (Cloud Storage, Cloud Functions, BigQuery). Build and optimize data pipelines, data storage architectures, and query performance. Troubleshoot high-impact issues, monitor system performance, and mentor junior engineers. Engage directly with clients and document business/technical requirements.

Hands-on Senior Data Engineer to design and maintain scalable AWS ETL/ELT pipelines, evolve a data lakehouse with Apache Iceberg, implement governance and security, optimize performance and costs, enable analytics-ready datasets, and ensure reliability, monitoring, and data quality.

As an ETL Data Engineer, you will design and maintain data integration processes using Python and Azure Synapse Analytics, ensuring accurate analytical data for decision-making while collaborating with data scientists and stakeholders.

As an ETL Data Engineer, you will design, develop, and maintain ETL processes primarily using Python and Azure Synapse Analytics, ensuring data accuracy for insights. You'll collaborate with teams, optimize performance, and implement data quality checks.

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, develop and maintain data integration processes using Python and Azure Synapse Analytics, ensuring data accuracy for insights.

The ETL Data Engineer will design, develop, and maintain data integration processes using Azure Synapse Analytics and Python. Responsibilities include data warehousing, data modeling, performance tuning, and collaboration with cross-functional teams.

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.

The Azure Data Engineer will develop and maintain ETL processes, optimize data storage, and enhance data quality using Azure Synapse Analytics and Python (PySpark). Responsibilities include data integration, modeling, and ensuring compliance with security standards.

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

As an ETL Data Engineer, you'll design, develop, and maintain ETL processes using Python and Azure Synapse Analytics, ensuring high data quality and performance. You'll collaborate with teams to enhance data management and implement data governance practices.

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