Data Engineer

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. As a Data Engineer specializing in Google's data platforms, you will design, build, and maintain data engineering solutions on Google's Cloud ecosystem. This role requires expertise in utilizing various Google services for batch and real-time data pipelines, data migration, and data layer design. Your primary responsibilities will include: • Design Data Pipelines: Design and develop batch and real-time data pipelines for Data Warehouse and Datalake using Google services such as DataProc, DataFlow, PubSub, BigQuery, and Big Table. • Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to build and maintain data engineering solutions. • Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), ensuring efficient data pipeline operations. • Implement Data Migration: Develop and implement data migration solutions using Google services, ensuring seamless data transfer between systems. • Optimize Data Layer: Design and optimize the data layer using Google services such as BigQuery, Big Table, and Cloud Spanner, ensuring efficient data storage and retrieval. Hands-on experience in data engineering Strong fundamentals in data engineering, including data modeling, ETL/ELT design patterns, best practices, and data pipeline performance tuning and optimization. Knowledge of data governance and security principles. Advanced SQL skills with a focus on query performance and optimization. Solid Python programming skills for data processing, automation, and scripting. Familiarity with distributed data processing frameworks such as Spark, Beam, Flink, or similar technologies. Good understanding of DevOps and data quality practices, including CI/CD for data workflows, version control, and testing frameworks for pipelines and transformations. Familiarity with AI-assisted development tools such as Gemini or similar technologies that support code generation, refactoring, or workflow automation. Advanced spoken English (B2 Level)  • Experience working with cloud platforms for data engineering (for example, AWS, GCP, or Azure). Mexico Data & Analytics Professional Mexico City, MX (0390) IBM de Mexico Comercializacion y Servicios