Top Data Engineer Jobs in Mexico

The Data Quality Engineer will implement a data quality framework, validate data pipelines, and collaborate with Data Engineering to ensure accurate data across platforms.

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.

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.

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.

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.

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.

Design, build, and operate scalable batch and real-time data pipelines and lakehouse solutions. Own end-to-end data flows, integrate ML/AI workloads with products, collaborate with cloud/DevOps teams, evaluate new technologies, and ensure production reliability and SLA-driven delivery.

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.

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.

Design, build, and productionize ML systems that process unstructured data (documents, transcripts, images, logs). Implement RAG, embeddings, semantic search, model serving, monitoring, CI/CD, and MLOps workflows. Collaborate with data scientists and engineers to deliver scalable, secure AI services and APIs for enterprise use.

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.

The ML Engineer will design, build, and operationalize AI systems for unstructured data, developing ML pipelines and collaborating with Data Scientists and Engineers to deliver scalable AI solutions.

Own cloud cost intelligence and operational health for Luma: analyze and optimize cloud spend, build dashboards and automated reporting, partner with stakeholders to align KPIs, enforce cloud governance, review architectures for cost impact, and improve data quality and observability to inform executive and engineering decisions.

Technical lead for a distributed, event-driven data processing and execution platform. Define architecture, migrate components to Node.js/TypeScript and Python, build asynchronous pipelines (Kafka/GraphQL/REST), ensure scalability, reliability, observability on Kubernetes/AWS, manage multi-store state consistency, and mentor engineering teams.

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.

As a Senior Data Engineer, you will design and maintain scalable data pipelines, collaborate on machine learning models, and support analytics initiatives for various business teams.

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.

Design, build, and maintain ETL pipelines and near-real-time data systems on AWS using Python and PySpark. Orchestrate workflows, implement event-driven integrations, optimize storage/querying (Redshift, RDS, S3), enforce data quality and monitoring, participate in CI/CD, and collaborate with stakeholders to deliver data solutions.

Designs, builds, and productionalizes enterprise data products and platform integrations using SQL, Python, Snowflake, and dbt. Owns data modeling, transformation logic, API and SaaS integrations, performance and cost optimization, and governance for analytics, ML, and natural language experiences. Leads complex initiatives, mentors engineers, and implements CI/CD and operational reliability standards.

The Senior Data Engineer will enhance Arkham's Data Platform with a focus on Lakehouse architecture, manage data pipelines, and ensure data quality across the lifecycle.

Design, build, and optimize scalable cloud data pipelines integrating enterprise sources (R&D, SAP, external). Transform raw data into business-ready datasets, ensure data quality and governance, automate ingestion and monitoring, support analytics/AI initiatives, and mentor junior engineers while collaborating with stakeholders to deliver data products and self-service solutions.

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.