Top Data Engineer Jobs in Mexico

Designs and develops scalable data pipelines, ETL/ELT processes, and integrations that synchronize enterprise data into Algolia indices. Optimizes indexing performance, data quality, and search relevance while implementing monitoring, governance, and validation. Collaborates with application, product, and business teams to define search requirements, troubleshoots synchronization and performance issues, and supports integrations with CMS, eCommerce, enterprise, and customer-facing platforms.

Designs and maintains scalable data pipelines, data models, semantic layers, data marts, and Tableau reporting solutions. Partners with stakeholders to translate reporting needs into technical solutions, while ensuring data quality, governance, performance, and reliability. Optimizes SQL queries, ETL/ELT processes, data warehousing, and Tableau performance across large-scale cloud data platforms. Collaborates with architects, analysts, and engineering teams on enterprise data solutions.

Leads end-to-end data migration projects, including profiling, quality assessment, source-to-target mapping, transformation logic, cleansing, validation, reconciliation, and UAT support. Collaborates with stakeholders to define requirements and acceptance criteria, recommends migration strategies, monitors risks, maintains governance and security standards, and produces documentation, reports, and quality metrics.

Designs and implements scalable data integration, migration, ETL/ELT, and data modeling solutions. Optimizes Elasticsearch indices and clusters, develops SQL queries and database objects, and builds integration services using Java, TypeScript, or C#. Supports data validation, reconciliation, CI/CD, testing, deployment, troubleshooting, architecture reviews, and enterprise data platform operations while collaborating with technical and business stakeholders.

Designs and maintains Coveo data ingestion and indexing pipelines, integrating enterprise data sources and configuring metadata, schemas, mappings, and enrichment. Optimizes search relevance using query pipelines, ranking strategies, and machine learning. Monitors analytics and platform performance, troubleshoots synchronization issues, supports data governance, and contributes to AI Search, Generative AI, and knowledge management initiatives. Partners with engineering, product, and business teams to improve enterprise information access.

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.

Design, build, and operationalize scalable data solutions and pipelines for customers. Analyze and transform data, ensure data quality/governance/security, test data movements, troubleshoot incidents, and collaborate with teams to support AI/ML and BI use cases.

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

Design, build, and maintain scalable cloud data pipelines and warehouses (primarily Snowflake and BigQuery). Optimize ETL/ELT, enable analytics/BI and ML use cases, implement governance/security, collaborate with distributed engineering and analytics teams, document lineage and metadata, and monitor and troubleshoot pipeline reliability.

Designs, builds, deploys, and operationalizes complex data engineering solutions for multiple customers. Responsibilities include Databricks migrations, data pipeline development, transformations, optimization, governance, security, quality, testing, incident resolution, and data source analysis. The role also involves advising customers, tracking project progress, collaborating across teams, and educating users on data products and analytics environments.

Own and build batch and real-time data pipelines to support marketing and fandom initiatives. Collaborate with stakeholders to model data, deliver high-quality datasets, enable analytics and ML, source data from APIs, and partner with data scientists and analytics engineers to drive business insights.

Lead design and implementation of cloud-native end-to-end data warehouses and ELT/ETL pipelines. Build scalable data models, enforce governance/security for U.S. financial regulations, optimize performance, and collaborate with U.S. executives to translate business needs into technical architecture.

Designs, develops, and maintains scalable batch and real-time data pipelines and curated datasets for enterprise credit risk, lending, and analytics. Builds solutions with Databricks and cloud-native platforms, implements data quality, monitoring, testing, and observability, and supports platform modernization. Partners with technical and business teams, troubleshoots production issues, contributes to architecture, improves reliability and efficiency, and mentors junior engineers.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Develop and maintain MicroStrategy and Logi Analytics reports, dashboards, metrics, datasets, and data visualizations. Write and optimize advanced SQL, validate and reconcile data, analyze BI logic, and work with stakeholders to clarify requirements. Apply relational database, data warehousing, and dimensional modeling knowledge. Use AI-assisted development tools daily, establish responsible AI coding standards, and evaluate LLM-based workflow and automation tooling.

Develop and maintain MicroStrategy and Logi BI reports, dashboards, metrics, datasets, and data visualizations. Analyze business logic, validate and reconcile data, optimize SQL queries, and work with relational databases, data warehouses, and dimensional models. Partner with stakeholders to clarify requirements and solve analytical problems. Use AI-assisted development tools daily, establish responsible AI coding standards, understand LLM fundamentals, and integrate AI into engineering workflows.

Design, develop, and implement end-to-end ETL/ELT pipelines, build complex queries and data transformations, establish data engineering best practices, collaborate with stakeholders, and support reporting and visualizations (Looker, Power BI).

As a Data Engineer, you will enhance the US Gas product by building data scrapers, improving APIs, and collaborating on features with the product team.

Design, architect, and operate an agentic data engineering ecosystem: build and govern autonomous agents for ETL, synthetic data, QA, and modeling; integrate secure context via MCP servers to Snowflake, Salesforce, and AWS; validate agent outputs and translate ambiguous business requests into production data products.

Lead data science and Generative AI initiatives using AWS Bedrock, foundation models, and LLMs. Provide production support, incident management, root-cause analysis, modernization, solution design, proof-of-concept development, high-quality Python coding, code reviews, testing, deployment, monitoring, documentation, and ongoing feature and defect maintenance. Collaborate with distributed teams and follow architectural decisions and agile delivery practices.

Build and maintain data products, pipelines, and orchestration workflows supporting supply chain analytics. Collaborate with data and business teams on scalable data platform strategy, source and transform data, troubleshoot data issues, write automated tests, monitor workflow failures, and develop solutions for business needs. Interns also apply Lean principles and continuous improvement practices while completing a meaningful business project.

Build and maintain ETL pipelines and recurring reports for content analytics, learner feedback, and business-impact cohorts. Maintain dashboards/headless analytics, run data quality checks, operate AI query tools, and support stakeholders with data validation and ad hoc requests, following established data architecture and standards.

Design, build, and maintain scalable Azure Databricks data pipelines and models. Refactor legacy code, create reusable components, enforce data quality and performance, and collaborate with analytics and engineering teams to support journey analytics and reporting.

Design, build, and optimize ETL pipelines and data architecture for renewable-asset systems. Collaborate with data science teams, validate data quality, unify microservice-based systems, and support data warehousing and lakehouse implementations in cloud environments.