Top Data Engineer Jobs in Mexico

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

Design, build, and maintain scalable ETL pipelines and a resilient data platform. Automate infrastructure and deployments, orchestrate workflows with Airflow, optimize performance, ensure data quality and security, and collaborate with engineering and DevOps teams to deliver cloud-based data solutions for financial services products.

Designs, develops, monitors and supports systems, applications and infrastructure; performs capacity planning, testing, diagnostics, and prototypes; collaborates with internal and external teams to resolve issues; develops tools for integration teams; ensures compliance with technology standards and risk controls.

As a Data Engineer, you will build and maintain data platforms, ensuring high-quality data availability for decision-makers, and develop ETL/ELT processes while utilizing data modeling techniques.

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.

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 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.

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 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.

Build, monitor, and troubleshoot data pipelines and transformations for healthcare data. Extract, cleanse, and load data; implement data quality checks; document datasets; support API and EFT integrations; triage incidents; and participate in Agile ceremonies to estimate and deliver data solutions.

The Senior Databricks Engineer will design, build, and maintain scalable data pipelines, optimize ETL/ELT processes, and ensure data quality and governance while collaborating with teams to meet data requirements.

The Data Engineer is responsible for designing and maintaining data pipelines and infrastructure, ensuring data accessibility and quality for analytics and reporting.

Lead data engineering initiatives, ensuring optimal architecture, manage teams, design scalable pipelines, and collaborate with data scientists to meet needs.

Design, develop, maintain and optimize ETL processes using Informatica PowerCenter. Build data transformation flows, develop Oracle SQL queries, create Shell scripts on Unix/Linux, monitor jobs with Control‑M, manage source control with Git/GitHub, and support Java microservice solutions following SOLID principles.

Lead design and delivery of Azure-based data platforms and Lakehouse architectures (Microsoft Fabric, Azure SQL, Power BI). Build scalable ETL/ELT pipelines, medallion-layered models, and AI-ready data feeds (embeddings/feature stores). Drive migrations from legacy systems, enforce governance and PII protection, mentor engineers, perform code reviews, optimize performance, and collaborate with product and business stakeholders to deploy analytics and reporting solutions.

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, build, and maintain production-grade data pipelines and models to power AI/ML workloads. Own pipeline reliability (orchestration, monitoring, incident response), collaborate with product and engineering to define requirements, optimize performance and cost, and document data models and runbooks.

Lead design and implementation of an enterprise data quality framework using Great Expectations, creating Rule-as-Code validations, observability, lineage, CI/CD integration, dashboards, and remediation processes while collaborating with cross-functional teams to prevent and resolve data issues.

Lead the end-to-end migration of a legacy SQL Server/SSIS data platform to a modern dbt-based ELT architecture. Implement Data Vault 2.0, build datamarts, design Dagster orchestration, run workloads on OpenShift/Kubernetes, enable Kafka-triggered near-real-time pipelines, and implement automated dbt testing, validation, and operational ownership in a regulated banking environment.

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.

Lead data quality and test automation for the enterprise data lake. Architect end-to-end testing frameworks across APIs, UI, and data pipelines; design data profiling and monitoring systems; use LLMs to accelerate test generation and engineering tasks; build internal automation tools and dashboards; ensure high-quality, compliant data products in cloud and distributed environments.

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.

Design, build, and operate core infrastructure for annotating and obfuscating sensitive data at scale. Collaborate with legal, product, compliance and engineering teams to embed privacy best practices. Make tradeoff decisions across privacy, security and productivity and improve engineering standards and processes.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

Design, build, deploy, and operate scalable cloud data, analytics, automation, and tooling solutions. Integrate Azure AI capabilities, implement monitoring/telemetry, apply engineering best practices, and collaborate cross-functionally to deliver customer-focused data experiences and evolve Microsoft’s data platforms.