Top Data Engineer Jobs in Mexico City

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

Design, build, and optimize scalable data pipelines and ETL/ELT workflows using Databricks, Spark, Delta Lake, and AWS. Develop data models for analytics, implement CI/CD and version control, and follow Medallion architecture to deliver reliable data solutions while collaborating with cross-functional teams.

Designs, implements, and maintains data pipelines and automations; integrates media, CRM, and digital data sources; manages tables in the data lake; and provides technical support to the analytics team.

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.

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 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.

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.

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

Build and maintain BigQuery data pipelines, map warehouse schemas, and craft SQL to feed LLMs. Develop Claude Skills and Gemini Gems, collaborate on prompts, validate AI outputs, and design AI agents to automate eCommerce analytics and operational workflows.

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.

As a Senior Data Engineer, you will design scalable data pipelines, develop data models, partner with cross-functional teams, and enhance data quality and governance.

Seeking a Data Engineer with 5+ years experience in ETL/ELT design, data analysis, and cloud-based solutions. Responsibilities include building data pipelines, maintaining data warehouses, and collaborating with stakeholders on data initiatives.

The Data Engineer role involves designing, developing, and optimizing data processing solutions ensuring quality and scalability, while adhering to project timelines and technical standards.

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.

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.

Manage the Databricks environment, implement data governance, oversee data architecture in Snowflake, and handle data pipelines across cloud platforms.

The Looker Data Engineer will develop and optimize Looker data models, automate reporting, and ensure data accuracy using SQL and various cloud technologies.

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

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.

Design, build, and maintain scalable data pipelines, Snowflake data models, and dbt transformations. Ensure ETL/ELT reliability, data quality, observability, and support analytics reporting for business stakeholders while collaborating cross-functionally.

The role involves data collection and management for AI model training at Speechify, focusing on cloud infrastructure and collaboration with AI researchers to enhance data ingestion pipelines.

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.