Top Data Engineer Jobs in Tijuana

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.

The Staff Engineer, Data Science role focuses on leveraging tech skills to enhance team capabilities and provide advanced solutions for client challenges.

Design, own, and scale the enterprise LookML semantic layer powering internal and client analytics. Build performant, reusable Looker data products, optimize queries with Analytics Engineering, enforce LookML style and governance, mentor junior developers, and enable conversational/AI-driven analytics across multiple Looker instances.

Design and maintain end-to-end data pipelines using Microsoft Fabric and Azure Synapse, ensuring data quality and performance optimization while collaborating with cross-functional teams.

Design and maintain data pipelines, optimize data processing with PySpark on Databricks, ensure data quality, and collaborate on data architecture solutions.

Drive quality assurance strategy for data migration projects, validating data integrity and automation using AWS technologies and Python scripts.

The role involves designing and maintaining data pipelines, developing ETL/ELT processes, building Power BI dashboards, and ensuring data quality and governance.

Design, build, and maintain ETL pipelines and orchestration on AWS using Python/PySpark. Integrate APIs and event-driven systems, optimize storage/querying (Redshift/RDS/Oracle/S3), implement data quality and monitoring, support CI/CD deployments, and collaborate with stakeholders to deliver data solutions.

Lead the migration of SQL Server and PostgreSQL to Azure SQL. Design and support PaaS and DBaaS offerings while optimizing database instances.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Lead design, build, and optimize scalable data architectures and end-to-end ETL pipelines. Ensure data quality, governance, performance monitoring, mentor engineers, and collaborate with cross-functional teams to deliver reliable data platforms.

Support design, build, and maintain AWS-based ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions), S3/Redshift/RDS/Oracle, write SQL, implement monitoring/logging, ingest API/JSON data, and assist deployment, documentation, and code management.

Design, build, and maintain ETL pipelines and near-real-time data systems on AWS using Python and PySpark. Orchestrate workflows, implement event-driven integrations, optimize storage/querying (Redshift, RDS, S3), enforce data quality and monitoring, participate in CI/CD, and collaborate with stakeholders to deliver data solutions.