Top Data Engineer Jobs in Mexico

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

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, build, deploy, and operate scalable data pipelines, products, and applications using software engineering best practices (Python, SQL, Snowflake). Improve DataOps/DevOps, implement CI/CD, automated testing, observability, and collaborate with architects and stakeholders to deliver reliable, discoverable data products.

Build and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production incidents. Collaborate with DevOps, platform, and analytics teams on security, observability, and compliance while contributing to Agile delivery and mentoring junior engineers.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, implement data quality and governance controls, troubleshoot production issues, and collaborate with DevOps and analytics teams. Mentor junior engineers and use AI coding tools to accelerate development, testing, and documentation.

Designs, builds, and operates batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Modernizes legacy ETL and warehouse workloads, optimizes Spark jobs and Delta tables, and implements governance, lineage, data quality, and access controls. The role includes troubleshooting production incidents, writing tested Python and SQL, collaborating with platform and analytics teams, participating in Agile delivery, and mentoring junior engineers.

Build and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed medallion-architecture Lakehouses, optimize Spark jobs and Delta tables, and implement data quality, lineage, and access controls with Unity Catalog. Troubleshoot production incidents, support analytics and machine learning consumers, and collaborate with DevOps and platform teams on observability, security, and compliance. Mentor junior engineers and contribute to Agile delivery practices.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, and implement data quality, lineage, governance, and security controls. Troubleshoot production data issues, migrate ETL workloads with validated parity, and collaborate with DevOps, platform, and analytics teams. Use Python, SQL, orchestration tools, and AI development assistants to deliver scalable production data platforms.

Design, build, and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into governed Lakehouse architecture, optimize Spark and Delta performance, implement data quality and lineage controls, and resolve production issues. Collaborate with DevOps, platform, and analytics teams on security and observability while participating in Agile delivery and mentoring junior engineers.

Design, build, and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into a governed Lakehouse with medallion architecture, optimized Spark jobs, data quality controls, lineage, and governance. Troubleshoot production incidents, write tested Python and SQL, collaborate with platform and analytics teams, participate in Agile delivery, and mentor junior engineers.

Design, build, and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into governed Lakehouse architectures, optimize Spark jobs and Delta tables, and implement data quality, lineage, and access controls. Troubleshoot production incidents, write tested Python and SQL, collaborate with platform and analytics teams, follow Agile practices, and mentor junior engineers.

Build and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, and implement data quality, lineage, governance, and access controls with Unity Catalog. Troubleshoot production incidents, maintain tested Python and SQL code, and collaborate with platform, DevOps, and analytics teams on secure, observable data platforms.

Build and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into a governed medallion Lakehouse, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production issues. Collaborate with DevOps, platform, and analytics teams on security, compliance, and observability while delivering scalable, well-tested Python and SQL solutions.

Build and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Modernize legacy ETL and warehouse workloads, optimize Spark and Delta performance, implement governance and data quality controls with Unity Catalog, and troubleshoot production issues. The role also requires writing tested Python and SQL, collaborating with DevOps and analytics teams, and using AI coding tools to accelerate development.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Migrate legacy data warehouse and ETL workloads, optimize Spark jobs and Delta tables, and implement data quality, governance, lineage, and access controls with Unity Catalog. Develop tested Python and SQL, troubleshoot production incidents, and collaborate with platform, DevOps, and analytics teams on secure, observable data platforms.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production incidents. The role requires strong Python, SQL, data modeling, workflow orchestration, streaming ingestion, migration, and collaboration skills, with optional Terraform, Azure DevOps, PostgreSQL, and monitoring experience.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Migrate legacy ETL and warehouse workloads, optimize Spark jobs and Delta tables, implement data quality and governance with Unity Catalog, and troubleshoot production incidents. The role requires advanced Python and SQL, streaming and workflow orchestration experience, secure coding practices, and collaboration with DevOps, platform, and analytics teams. AI coding tools such as Claude and GitHub Copilot are used to accelerate development.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Modernize legacy data warehouses and ETL workloads, optimize Spark and Delta performance, and implement data quality, lineage, governance, and access controls with Unity Catalog. Develop tested Python and SQL, troubleshoot production incidents, and collaborate with DevOps, platform, and analytics teams. Use AI coding tools to accelerate development. The role is fully remote.

Design, build, and support data services, pipelines and internal data architecture. Collaborate on product development, analyze and organize raw data, improve data quality, define best practices, and identify acquisition opportunities to support business insights and member-facing services.

Develop and maintain Tableau dashboards, workbooks, data sources, and interactive visualizations. Build and optimize SQL queries, data models, analytics-ready datasets, and ETL/ELT pipelines using Snowflake and enterprise data sources. Perform data profiling, validation, reconciliation, troubleshooting, testing, documentation, and performance optimization while translating business requirements into KPIs and reporting solutions.

Design, integrate, and implement on‑prem and cloud data warehousing solutions. Process payer files into the data warehouse, write SQL/Python/PySpark code, perform dimensional modeling, troubleshoot data issues, improve performance, and collaborate with engineers, analysts, data scientists, and product teams.

Develop and maintain Python-based data and cloud migration frameworks for Snowflake and Azure. Migrate databases, stored procedures, analytic scripts, and potentially web applications to cloud platforms. Build scalable software solutions, participate in Agile delivery, testing, code reviews, technical design, requirements gathering, documentation, and technology evaluation. Collaborate with engineers, designers, managers, and financial-market users while maintaining quality, reporting progress, and owning projects through launch.

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.

Lead the design and implementation of scalable on-premises and cloud data platforms. Build and maintain ETL/ELT pipelines, support data governance and quality, participate in architecture and planning, and mentor engineers through code reviews and technical guidance. The role requires strong Python, Spark, Hadoop, SQL, Databricks, orchestration, DevOps, and CI/CD expertise, along with effective communication in English and Spanish.

Build and maintain large-scale ETL/ELT pipelines using Databricks, Spark, Python, Hadoop, and cloud data platforms. Develop and optimize PySpark data processing jobs, improve pipeline performance and cost, and support CI/CD, testing, deployment automation, and observability. Collaborate with Product Managers to define user-focused data modules and features. Debug complex data issues and communicate effectively with technical and non-technical stakeholders.