Top Data Engineer Jobs in Mexico

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.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable 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.

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.

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.

The Lead Data Integration Engineer will own the data integration strategy, provide technical leadership, and develop scalable integration solutions for the Wealth Lending 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.

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 maintain AI-powered applications and automation workflows; integrate AI with enterprise systems, databases, and APIs; maintain legacy and Ruby production systems connected to Snowflake; develop ETL pipelines and reports; work in Linux environments, manage remote systems, reverse engineer undocumented systems, and collaborate with BI, analysts, and stakeholders to deliver practical, business-focused AI solutions.

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.

Develop, analyze, test, debug and enhance application systems as part of the Technology team. Build and support big data solutions using PySpark/Scala, streaming (Kafka), data warehouse/marts, cloud platforms, and ensure compliance with policies and risk controls.

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.

Lead operations engineer responsible for installing, configuring, monitoring, troubleshooting and maintaining secure data transfer systems. Collaborates with customers, vendors and internal teams, writes automation scripts and AI prompts, manages servers, networks, firewalls, keys and encryption, and uses cloud and API integrations to ensure reliable data flows.

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.

Entry-level role supporting data platform reliability, observability, and AI infrastructure. Build and maintain monitoring, automation, and IaC using Python, SQL, Terraform, and Azure while collaborating with visualization, engineering, DBA, and ML teams.

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

The Software Engineer will support data collection for AI model training, manage cloud infrastructure, and collaborate with scientists to enhance data quality and throughput.

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 and build scalable Databricks-based data and GenAI pipelines, implement RAG/vector search, integrate LLMs for enrichment and anomaly detection, create natural-language data interfaces, ensure data quality and traceability, and support CI/CD automation across analytics and engineering teams.

As a Senior Data Engineer, you will design, build, and deploy data solutions, guide customers, and ensure data quality and governance.

Design, build, and operate scalable data pipelines and cloud solutions, focusing on monitoring and automation while working with AWS services.

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.

As a Senior Data Engineer, you will design, build, and maintain scalable data platforms, ETL pipelines, and support analytics and ML workflows, ensuring data quality and cross-functional collaboration.