Top Data Engineer Jobs in Mexico

Design, optimize, and productionize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data validation, troubleshooting, and operational automation. Develop reusable frameworks, implement monitoring and quality controls, optimize performance, support deployments, and resolve production issues while collaborating with business, data engineering, and platform teams.

Develop and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data validation, troubleshooting, automation, and production operations. Implement quality controls, monitoring, testing, deployment, performance optimization, and reusable data engineering frameworks. Collaborate with business, engineering, and platform teams to productionize reliable data and AI-enabled solutions.

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.

Build and scale data pipelines, models, and curated datasets for People Analytics. Integrate HR, recruiting, and employee listening data; implement validation, reconciliation, monitoring, and documentation; support reporting across hiring, retention, mobility, skills, and engagement. Partner with HR stakeholders and People Analysts to translate business questions into reliable analytical solutions, using AI-assisted approaches to improve development and data quality.

Designs, develops, deploys, and supports scalable data pipelines, products, and applications. Builds Python and SQL solutions on Snowflake, implements dbt modeling and automated testing, and manages CI/CD, DevOps/DataOps, observability, and production support. Partners with architects, analysts, product teams, and engineers to translate requirements into trusted data products with clear SLAs. Promotes engineering standards, reusable patterns, data governance, reliability, and continuous improvement.

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.

Build and maintain scalable data pipelines, ETL/ELT processes, data models, metric definitions, and data infrastructure. Develop production solutions with Python, SQL, Spark, dbt, Fivetran, Airflow, Databricks, Azure, and cloud storage. Collaborate with data scientists, engineers, analysts, and stakeholders to deliver reliable, accessible data and self-service tools. Troubleshoot pipeline issues, improve data quality and scalability, and use AI-assisted development tools to accelerate engineering work.

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.

Designs and maintains scalable cloud-based data infrastructure, pipelines, ETL processes, and data warehouse solutions using Python, SQL, PySpark, Spark, and Databricks. Collaborates with stakeholders to define data requirements and ingestion architecture, troubleshoots data quality and performance issues, documents solutions, and develops automated testing for data pipelines, APIs, web services, and microservices. The role also supports CI/CD, cloud platforms, streaming validation, and large-scale data projects.

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 operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Build and enhance data engineering frameworks, scalable data solutions, centralized ETL logic, metric definitions, and concise data models. Develop self-service data tools supporting data science, internal applications, and organizational decision-making. Collaborate with cross-functional stakeholders, use AI coding agents such as Claude Code or Codex, and write production-level Python and SQL.

Design and maintain scalable Snowflake data models and dbt projects supporting analytics and reporting. Build ELT/ETL pipelines from ERP, MES, IoT, API, and file-based sources using AWS services and Python. Optimize Snowflake performance and costs, implement data quality and governance practices, and support lineage and documentation. The role requires advanced SQL, Python, cloud platform knowledge, BI tool experience, and production experience with Snowflake and dbt.

Develop and support applications and data systems using Python, PySpark, ETL processes, data warehousing, and data modeling. Analyze requirements, design and test solutions, optimize ETL performance, support business users, implement security measures, and advise stakeholders. The role requires experience with big data technologies, databases, cloud platforms, Unix/Linux environments, and financial or investment banking data workflows.

Designs, builds, and maintains scalable data pipelines, warehouses, data models, and integration frameworks supporting BI, reporting, AI, and analytics. The role enables Power BI datasets, improves data quality and governance, supports Azure data platforms, and partners with IT, business stakeholders, and analytics teams. Responsibilities include ETL/ELT development, data architecture, monitoring, automation, semantic modeling, API integration, and preparing governed data assets for advanced analytics and AI initiatives.