Top Data Engineer Jobs in Mexico

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Design, develop, deploy, and operate scalable cloud-based data, analytics, automation, and tooling solutions. Integrate Azure AI and machine learning capabilities, implement monitoring and telemetry, analyze usage data, and collaborate with cross-functional teams to deliver customer-focused systems. Own solutions across design, implementation, and operations while contributing to Microsoft’s data platform innovation.

Build and manage HR data pipelines, ETL/ELT processes, data warehouse models, and analytics workflows across cloud platforms. Ensure data quality, governance, security, and availability for People Analytics and HR stakeholders. Lead and mentor data engineers, collaborate with business and IT teams, and support reporting and analysis using tools such as Python, R, and business intelligence platforms.

Develop and manage HR data pipelines, ETL processes, warehouse models, and data transformations for People Analytics. Integrate data from Workday, payroll, talent management, and other systems while ensuring quality, security, governance, and compliance. Mentor data engineers, support reporting workflows in Python and R, and collaborate with HR, IT, and business stakeholders to deliver reliable analytics solutions.

Accelerate business definition capture and certification in Unity Catalog. Partner with data stewards and domain owners across Finance, Commercial, HR, and Marketing to map assets, facilitate workshops, resolve terminology conflicts, validate inputs, upload approved definitions, and establish lightweight governance. Improve the Excel and Python intake process, document workflows, and ensure definitions remain accurate, current, and discoverable.

Design and maintain scalable Snowflake data models, dbt projects, and ELT/ETL pipelines. Optimize Snowflake performance and costs, develop Python-based workflows, and integrate data from ERP, MES, IoT, APIs, and files. Implement data quality, governance, lineage, testing, documentation, and CI/CD practices. Support analytics and reporting through optimized datasets and BI tools such as Power BI and Tableau.

Design, build, and optimize scalable ETL/ELT pipelines and data models using dbt, Snowflake, Informatica, and Airflow. Integrate data from multiple systems, ensure data quality and governance, support analytics and machine learning use cases, and troubleshoot pipeline performance issues. Collaborate with analysts, product managers, and business teams to deliver reliable data solutions while improving engineering processes, documentation, and platform scalability.

Leads the design and development of scalable on-premises and cloud-native data platforms. Builds production-grade ETL/ELT pipelines using big data technologies, supports governance and data quality, and contributes to architecture and planning. Mentors engineers, reviews code, solves complex data issues, and promotes maintainable, testable solutions. The role requires Python, Spark, Hadoop, SQL, Databricks, orchestration tools, and DevOps practices, with Java experience preferred.

Designs, develops, and supports scalable data pipelines, integrations, data models, warehouses, and enterprise data products. The role integrates internal, cloud, API, and vendor data while ensuring quality, governance, performance, and reliability. Responsibilities include stakeholder collaboration, technical leadership, Agile project support, vendor partnership, and continuous improvement of data architecture and platform capabilities.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build scalable data migration solutions for enterprise customers using Python, SQL, ETL, APIs, automation, and containerized tooling. Partner with customers, architects, project managers, and product teams to understand requirements, troubleshoot integrations, validate and reconcile data, and improve migration frameworks. Use AI-assisted development tools responsibly while maintaining code quality, security, testing, documentation, and delivery standards.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

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.

Designs and maintains automated AWS data pipelines using Python, Airflow, dbt, SQL, and cloud storage. Responsibilities include pipeline orchestration, error troubleshooting, data imports and exports, Snowflake query optimization, reusable code generation, testing, security, and GitHub-based CI/CD. The role also supports AWS services, API integrations, monitoring, data quality validation, and collaboration with global teams in English and Spanish.

Designs and maintains production data transformation systems using dbt, SQL, cloud data platforms, incremental models, macros, tests, snapshots, packages, documentation, and lineage. The role also establishes standards for AI-assisted development, evaluates AI tooling, integrates agentic automation into developer workflows, and applies LLM fundamentals to implementation, testing, code review, and documentation.

Leads the design and implementation of scalable data ingestion, transformation, and ELT pipelines using SQL, dbt, and AWS Athena. Owns data modeling, Medallion Architecture, data quality frameworks, validation, monitoring, query optimization, documentation, and continuous improvement. Provides technical leadership in data engineering and governance, identifies pipeline risks and bottlenecks, and collaborates directly with clients.

Build and optimize batch and streaming data pipelines, semantic models, and transformations on Databricks using Python and Spark. Define data contracts, quality rules, and data SLOs; guide domain teams on data modeling and product boundaries; mentor engineers and analysts; and improve reusable platform templates, tooling, and engineering practices.

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, operate, and scale change data capture pipelines using Debezium, Kafka or Azure Event Hubs, and Databricks. Manage streaming data across hundreds of databases and thousands of tables, ensuring reliability, low latency, schema consistency, and data quality. Troubleshoot replication lag, schema drift, backpressure, and recovery issues. Collaborate with backend and data teams on schemas and SLAs while documenting architecture and operational procedures.

Develop and maintain Python-based data pipelines, ETL/ELT processes, and enterprise data integration solutions. Support Ab Initio applications and workflows, implement data quality and validation controls, optimize performance, troubleshoot production issues, and integrate systems with databases, cloud platforms, and orchestration tools. Collaborate with technical and business stakeholders, participate in testing and deployment, document solutions, and follow governance and security standards.

Build and operate the people-data platform supporting analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, governance, privacy, and access controls. Monitor reliability, manage incidents, document technical assets, and define infrastructure improvements. Own the people data lake’s AI-readiness roadmap and collaborate with the People Analytics Product Owner, BI Developer, enterprise data, HR technology, security, legal, and privacy teams.

Build and own an ETL pipeline replacing a third-party ingestion tool. Document legacy ingestion logic, ingest Amazon DSP and other digital advertising data, evaluate Fivetran versus custom infrastructure, and align pipeline outputs with attribution, scoring, dashboards, and data science models. Provide broader backend and application engineering support within the existing AWS environment.