Top Data Engineer Jobs in Mexico

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Design, implement, optimize and monitor data ingestion, storage, recovery and delivery systems. Provide data management, BI/reporting support and data architecture expertise across the organization.

Mastercard seeks a Site Reliability Engineer focused on data protection to support and improve the Commvault backup environment, storage platforms (VAST, NetApp, Oracle ZFS, Data Domain), monitoring/observability (Grafana, Prometheus), and automation (Chef, Ansible, Jenkins, Bitbucket, Python/Bash). Responsibilities include troubleshooting Unix/Linux/Windows, designing automation/monitoring solutions, supporting disaster recovery, documenting designs/processes, and collaborating across global teams to enhance reliability and operational excellence.

Design, build, and optimize scalable data pipelines and storage on Azure (data lakes/warehouses). Integrate and transform data from databases, APIs, and streaming sources; tune performance; collaborate with data scientists and stakeholders; work in Agile; mentor junior engineers and deliver robust, production-ready solutions with CI/CD practices.

Design, build, and maintain scalable ETL and data pipelines on AWS using Python and PySpark. Develop Glue/Lambda/Step Functions workflows, event-driven integrations, and data lake/warehouse solutions. Implement data validation, monitoring, and error-handling; tune SQL and performance; support APIs/JSON integrations; troubleshoot production issues; document, automate deployments, and mentor junior engineers.

Support design, build, and maintenance of ETL pipelines using Python and PySpark. Develop workflows with AWS Glue, Lambda, and Step Functions. Work with S3, Redshift, RDS, and Oracle; write complex SQL for extraction, transformation, validation, and reporting. Implement monitoring, logging, and error handling. Ingest/process API and JSON payloads. Collaborate with engineers, analysts, and stakeholders; contribute to code management, documentation, and deployment support.

Lead design, build, and maintain automated data pipelines and BI solutions. Modernize manual reports into scalable workflows, optimize SQL and data models, support model scoring and production data prep, monitor data quality, troubleshoot pipelines, and collaborate with stakeholders to deliver reliable analytics.

Support migration of archived Concur data to AWS S3 using AWS Glue Catalog and Amazon Athena. Upload and organize data in S3, create Glue Catalog tables from DDL, configure Athena, develop and run SQL queries for data retrieval and validation, interpret relational schemas, join and validate datasets, and perform basic file handling, extraction, and structured data analysis.

Design, build, and maintain ETL pipelines, dashboards, and corporate data architecture for Sales, Finance, HR, and software teams. Improve data quality, automate processes, create AI agents, port dashboards to new architectures, and collaborate with engineering to scale data solutions.

Build, maintain, and optimize scalable ETL/ELT data pipelines and data solutions to ensure data quality, reliability, and performance. Support data ingestion, transformation, delivery, modeling (star schema/dimensional), debugging, and pipeline performance improvements while collaborating with global stakeholders.

Design, build, and maintain Databricks data pipelines using PySpark, SQL, and Delta Lake. Implement medallion architecture, ensure data quality and lineage, optimize performance, troubleshoot production issues, document pipelines, and collaborate with stakeholders. Support version control, testing, deployment, and improvements; assist with migrating Alteryx workflows and building Power BI visualizations.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

Validate and document an existing enterprise data ecosystem by analyzing SQL, stored procedures, ETL jobs, and data transformations; trace data flows, confirm business-rule implementation, and collaborate with stakeholders to produce accurate technical documentation.

Design, integrate, and implement on‑prem and cloud data warehousing solutions. Process payer files into the data warehouse, write SQL/Python/PySpark code, perform dimensional modeling, troubleshoot data issues, improve performance, and collaborate with engineers, analysts, data scientists, and product teams.

Build and maintain end-to-end AWS data pipelines: design reusable Python packages, implement ETL/Orchestration (Airflow), validate data with DBT/tests, optimize SQL for Snowflake/Redshift, integrate cloud services (S3, Lambda, EMR), implement CI/CD in GitHub, and monitor pipelines (CloudWatch, DataDog). Client-facing, hands-on individual contributor with strong troubleshooting and documentation responsibilities.

Design, build, and maintain data pipelines and ETL/ELT processes to transform raw data into business-ready datasets. Monitor data quality, integrate data via APIs and cloud platforms, support lightweight data applications, and collaborate with business stakeholders on governance, security, and automation improvements.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture; integrate multiple data sources; ensure data quality, governance, security, and performance; monitor pipelines; support BI, analytics, and ML; document models and workflows; collaborate with stakeholders.

Designs and builds scalable systems for collecting, storing, and analyzing university data. Implements and maintains a data lake, readies data environments for AI, ensures security and governance, provides technical support, develops data transformation/validation algorithms, and participates in system upgrades and project implementation.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

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.

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

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.