Top Data Engineer Jobs in Mexico City

Design, build, and scale data pipelines and analytics solutions across the full data lifecycle. Implement ETL, data modeling, and high-volume data systems using Spark/Scala, Python, Databricks, Airflow, and AWS. Collaborate with stakeholders, contribute to architecture and CI/CD practices, and deliver projects on schedule.

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 AI Engineer & Data Engineer will develop data pipelines, ensure data quality, and integrate AI into workflows for performance reporting and billing reconciliation.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

As a Data Engineer, you will enhance the US Gas product by building data scrapers, improving APIs, and collaborating on features with the product team.

The Senior AI/ML Engineer will architect and develop AI/ML solutions, collaborate with teams, and ensure deployment compliance, especially in healthcare.

Develop and enhance Zillow's analytics platform. Responsibilities include building reporting tools, improving data discoverability, and designing AI-driven workflows. Collaborate with teams to ensure reliable self-service analytics and manage projects from conception to deployment.

Lead architecture and scaling of the enterprise data platform. Build and modernize serverless, production-grade data pipelines (Airflow, dbt, Python), integrate tools like Fivetran and Redshift, implement observability and testing, mentor engineers, and drive data governance and cross-functional alignment for analytics and operational reporting.

This role involves finding audio data sources, managing cloud infrastructure for data ingestion, and collaborating on dataset roadmaps for AI models.

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

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.

Lead the migration of SQL Server and PostgreSQL to Azure SQL. Design and support PaaS and DBaaS offerings while optimizing database instances.

Design, build, and maintain ETL pipelines and analytics-ready data warehouses for AI/ML features. Ensure data quality, security, and compliance; automate ingestion and transformations; manage complex datasets; collaborate cross-functionally; research and prototype new data solutions; and mentor junior engineers while owning production deployments and documentation.

The Data Engineer is responsible for designing and maintaining data pipelines and infrastructure, ensuring data accessibility and quality for analytics and reporting.

The Data Engineer will design and develop data pipelines using Azure Data Factory, migrating existing integrations from Boomi, and collaborating with teams to ensure efficient data processes and migration testing.

Build, deploy, and support scalable data platforms and pipelines to enable AI use cases. Partner with scientists, engineers, and product teams to translate requirements, optimize production data for scale and low latency, improve platform reliability, and contribute shared tooling and operational excellence.

Build and maintain integrations to retrieve, normalize, and store financial and non-financial data at scale. Develop crawlers, extractors, and a large-scale data extraction platform, reverse-engineer anti-bot solutions, work with headless browsers and proxies, design asynchronous multi-service architectures, ensure performance and reliability, and collaborate across cross-functional teams while leveraging AI tools and secure practices.

Design, build, and maintain batch and real-time ETL/ELT data pipelines and workflows. Collaborate with US-based stakeholders to translate requirements into technical solutions, implement code, ensure data quality, logging, security, and compliance, and mentor other data engineers. Work with cloud data warehouses, data lakes, big data technologies, visualization tools, and pipeline orchestration frameworks.

Design and maintain end-to-end data pipelines using Microsoft Fabric and Azure Synapse, ensuring data quality and performance optimization while collaborating with cross-functional teams.

Design and maintain data pipelines, optimize data processing with PySpark on Databricks, ensure data quality, and collaborate on data architecture solutions.

The role involves designing and maintaining data pipelines, developing ETL/ELT processes, building Power BI dashboards, and ensuring data quality and governance.

Design, build, and scale cloud-based data pipelines and reporting infrastructure for advertising performance and billing reconciliation. Ensure data quality, secure handling of financial data and PII, modernize legacy SQL Server ETL, implement IaC and CI/CD, and collaborate with Product, Engineering, Analytics, and business stakeholders to enable reliable, automated data consumption.

The Data Quality Engineer ensures the quality and integrity of data in studies, developing measurement systems, automation, and conducting analysis to uphold standards across projects.

As a Senior Data Engineer, you'll design and implement data solutions, optimize data pipelines, ensure data quality, and collaborate with cross-functional teams to support analytics initiatives.