Top Data Engineer Jobs in Mexico

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.

Build and maintain Tableau or Power BI dashboards and reports; analyze manufacturing, operational, and financial data; query Snowflake with SQL; support dbt data models; document metrics; and train business users in self-service analytics.

The Data Architect role involves defining the data strategy, optimizing data schemas, enhancing performance, and collaborating with multiple teams for effective data management.

The Data Engineer I will design and manage data pipelines, integrate ETL processes, ensure data quality, and optimize performance while working with various cloud and on-premises technologies.

The Azure Data Engineer will participate in the software development lifecycle, including design, coding, testing, and documentation of applications, collaborating closely with team members and following best practices.

Lead AWS Data Engineer responsible for designing and implementing cloud-native data solutions, overseeing data migrations, and mentoring engineering teams.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, and maintain scalable data pipelines, data vault, data warehouse, and marts. Integrate TMS and other sources, tune SQL performance, collaborate with stakeholders to deliver operational and business metrics.

Develop and execute manual and automated tests for data-focused applications, APIs, databases, and pipelines. Translate requirements into test plans, validate data quality and integrity, track defects, collaborate with engineering and stakeholders, and improve testing processes, automation, coverage, and efficiency using AI tools.

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.

Designs, develops, and maintains scalable data pipelines and models in Databricks and Microsoft Azure. Responsibilities include data ingestion, transformation, integration, performance optimization, troubleshooting, migration, governance, documentation, and collaboration with architecture and engineering teams. The role requires translating business needs into reliable technical data solutions and supporting analytical and operational use cases.

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.

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.

Develop SQL queries, optimize databases, and manage indexing, stored procedures, window functions, and dimensional models. Build advanced Power BI Report Server dashboards using DAX and Power Query. Own the full data lifecycle, including mastering, ETL, data modeling, KPI development, and visualization. Collaborate with business stakeholders to translate requirements into analytical solutions. AI tooling proficiency is preferred for workflow optimization and automation.

Build and maintain secure, scalable data pipelines and platform components using AWS, Snowflake, dbt, and Airflow. Modernize legacy data flows, integrate batch and streaming sources, improve testing and observability, and support self-service analytics. Develop AI-enabled capabilities such as anomaly detection and automated tagging. Partner with Finance, Risk, Product, Analytics, ML, and Engineering teams to define requirements, meet SLAs, ensure data governance, and deliver reliable infrastructure end to end.

Lead enterprise-scale data engineering initiatives using Python, Spark, Kafka, and Azure services. Design reusable data-processing frameworks, data models, cloud-native platforms, AI-powered solutions, and automation systems. Mentor engineers, lead architecture and technical decisions, troubleshoot complex systems, influence business and regulatory requirements, and align data strategy with enterprise objectives.

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.

Build and maintain ETL and data integration pipelines using Informatica/IICS, Python, MuleSoft, Snowflake, and Airflow. Responsibilities include transforming and validating data, monitoring and troubleshooting pipeline jobs, managing API connections and database access, supporting environment promotions and CI/CD deployments, resolving production issues, documenting operational processes, and collaborating with analytics, platform, infrastructure, and business teams.

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.

Design and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines across AWS, Azure, and GCP. Build Spark and Delta Lake workflows for structured and unstructured data, tune performance and cloud costs, and integrate MLflow for AI model operationalization. Implement CI/CD and infrastructure-as-code deployments, enforce governance and security through Unity Catalog, and collaborate with client leaders to deliver production-ready data and AI solutions.

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.

Own end-to-end data engineering projects and build scalable ELT pipelines using Snowflake, dbt, and Airflow. Responsibilities include performance tuning, dimensional modeling, data warehousing, BI data preparation, platform modernization, automation, risk identification, and mentoring engineers. The role also supports future AI/ML data capabilities and requires strong independent delivery in a post-acquisition SaaS data environment.