Top Data Engineer Jobs in Mexico City

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.

As a Senior Software Engineer, you will develop core data pipeline components for an AI-native data warehouse, oversee feature delivery, and ensure data quality and reliability.

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.

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.

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.

The Principal Engineer will oversee data infrastructure, optimizing databases, and facilitating data systems for high volume processing while integrating new technologies.

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.

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

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.

The Senior Data Engineer will design, implement, and optimize data platforms, ensuring performance and quality in an agile environment.

We are seeking experienced Senior Data Engineers to design and optimize data systems and architectures using cloud technologies to support data pipelines.

The Machine Learning Engineer / Data Scientist will build and deploy machine learning solutions, translating business needs into ML problems, working with data analysis, model development, and productionization tasks.

The Senior Data Engineer will design and maintain ETL pipelines, automate report generation, and ensure data quality while collaborating with various teams.

Lead architecture and implementation of scalable cloud-native data platforms and medallion-tiered pipelines. Optimize ETL/ELT, enforce data governance, security, and CI/CD/IaC. Mentor engineers, drive adoption of managed/serverless cloud services, integrate BI tools, and lead client-facing technical consulting and architectural decisions.

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.

Design, build, deploy and monitor ML models for fraud detection, credit risk, churn, cross-sell and collections. Handle large datasets, feature engineering, production-grade code, and scalable feature/model pipelines. Collaborate with risk, fraud, engineering and product teams and integrate new data sources to improve model performance in international markets.

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.

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.

The Senior Data Engineer will develop and optimize data solutions, manage ingestion and transformation pipelines, and ensure data quality and operational reliability in AWS and Snowflake environments.

Lead the development, optimization, and scaling of data solutions. Design and maintain data pipelines using Databricks and collaborate with multidisciplinary teams to define requirements.