Top Data Engineer Jobs in Mexico City

Design and build self-service analytics portals and responsive dashboards. Integrate data pipelines from Databricks, S3, Salesforce and internal APIs. Implement secure auth and role-based access. Collaborate with product and data teams, remain hands-on, and mentor junior developers to deliver scalable, customer-facing analytics experiences.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

Lead design and delivery of end-to-end healthcare data platforms using Snowflake, dbt, and Azure services. Model EHR/claims/lab data, build and optimize pipelines (Python/SQL), ensure regulatory/privacy/security compliance, apply data quality frameworks, run stakeholder workshops, and leverage AI tools to accelerate development and governance.

The Principal Agentic Data Systems Engineer designs and oversees a digital workforce, enhancing workflows through autonomous agents for data processing, governance, and analytics. The role demands expertise in data architecture, AI orchestration, and technical leadership in a rapidly evolving AI environment.

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.

Build and maintain data pipelines, dbt transformations, SQL models, and analytics datasets. Develop Power BI dashboards, semantic models, and DAX measures while validating data quality and troubleshooting issues end to end. Translate business needs into reporting solutions, support existing analytics environments, and independently deliver production-ready work. Collaborate with business, analytics, and engineering teams while applying AI tools to improve productivity.

The Lead Agentic Data Systems Engineer will design and build production-grade data products, maintain AI-driven systems, and ensure effective orchestration of autonomous agent workflows.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Senior UI developer responsible for designing and building responsive React-based UIs and dashboards for cybersecurity analytics. Develop backend services and APIs in Python, integrate security data sources, optimize SQL Server/PostgreSQL reporting, collaborate with stakeholders, influence architecture, and mentor junior developers.

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.

Senior integration engineer responsible for designing and implementing API integrations, ETL pipelines, and Power Platform connections; building and maintaining APIs and data transformation workflows; ensuring data quality, security, reliability, and optimizing/troubleshooting integration flows.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.

The Staff Engineer, Data Science role focuses on leveraging tech skills to enhance team capabilities and provide advanced solutions for client challenges.

Design, own, and scale the enterprise LookML semantic layer powering internal and client analytics. Build performant, reusable Looker data products, optimize queries with Analytics Engineering, enforce LookML style and governance, mentor junior developers, and enable conversational/AI-driven analytics across multiple Looker instances.

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.

Drive quality assurance strategy for data migration projects, validating data integrity and automation using AWS technologies and Python scripts.

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

Build and operate production-grade agentic data products by implementing multi-agent ETL, synthetic data, QA, and modeling pipelines. Architect agent orchestration, verification protocols, MCP servers for secure data access, and governance-as-code. Translate ambiguous business needs into validated data products, run red-team security agents, and maintain cloud-native infrastructure and data mesh/semantic layers.

Design, build, and maintain ETL pipelines and orchestration on AWS using Python/PySpark. Integrate APIs and event-driven systems, optimize storage/querying (Redshift/RDS/Oracle/S3), implement data quality and monitoring, support CI/CD deployments, and collaborate with stakeholders to deliver data solutions.

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

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead design, build, and optimize scalable data architectures and end-to-end ETL pipelines. Ensure data quality, governance, performance monitoring, mentor engineers, and collaborate with cross-functional teams to deliver reliable data platforms.

Support design, build, and maintain AWS-based ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions), S3/Redshift/RDS/Oracle, write SQL, implement monitoring/logging, ingest API/JSON data, and assist deployment, documentation, and code management.