Top Data Engineer Jobs in Mexico

Support product development by managing product specifications, executing data migrations, running pilot-plant and factory trials, performing complex Excel-based formulation conversions, and coordinating stakeholders to ensure specification completeness and compliance with HACCP/GMP.

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.

Design and deliver complex GIS data engineering and ingestion projects. Transform, integrate, and maintain large-scale geospatial datasets using FME, ArcGIS, QGIS, Python, SQL, Oracle, and PostgreSQL. Assess risks for bulk ingestion, provide spatial data consultancy, improve processes and workflows, collaborate cross-functionally, and mentor junior colleagues.

As a Data Engineer, you will design and build scalable analytics pipelines, manage data integrations, and optimize data architecture for large-scale projects.

As a Senior Data Engineer, you will design scalable data pipelines, develop data models, partner with cross-functional teams, and enhance data quality and governance.

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.

The Data Engineer is responsible for designing and maintaining data pipelines and infrastructure, ensuring data accessibility and quality for analytics and reporting.

Analyze existing integrations and rebuild ETL/ELT pipelines in Azure Data Factory. Design, develop, schedule, monitor, and optimize data movement and transformations. Connect ADF to sources/targets (Oracle, Azure SQL, files, APIs), implement parameterization, retry logic, and alerting. Perform mapping, validation, testing, documentation, deployments, and production cutover while collaborating with database engineers, QA, architects, and business teams.

Design, develop, maintain and optimize ETL processes using Informatica PowerCenter. Build data transformation flows, develop Oracle SQL queries, create Shell scripts on Unix/Linux, monitor jobs with Control‑M, manage source control with Git/GitHub, and support Java microservice solutions following SOLID principles.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

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).

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.

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, 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 and build scalable Snowflake-based data models, analytical cubes, and high-performance data pipelines using SQL and Python. Integrate data via APIs, collaborate with cross-functional teams to support reporting and analytics, and enforce best practices in data architecture and pipeline performance.

The Data Engineer builds and manages data pipelines, focusing on ingesting and transforming data within a data lake, ensuring compliance with requirements, and collaborating with data teams.

The Staff Data Engineer will lead data model design, standardize data engineering practices, modernize orchestration, and establish governance strategies within Dropbox's Analytics team.

Build and maintain large-scale data structures and ETL pipelines using Python and SQL. Design data marts and models, integrate data sources, ensure data quality, and collaborate with cross-functional teams to operationalize analytics on cloud platforms.

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 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.

The Looker Data Engineer will develop and optimize Looker data models, automate reporting, and ensure data accuracy using SQL and various cloud technologies.

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.