Data Engineer-Data Platforms-Google - 4

Introduction 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.

Your Role And Responsibilities As a Data Engineer specializing in Google's data platforms, you will design, build, and maintain data engineering solutions on Google's Cloud ecosystem. You will utilize various Google services to develop batch and real-time data pipelines, manage data migration, and design data layers.

Your Primary Responsibilities Will Include

• Design Data Pipelines: Design and build data pipelines using Google DataProc, DataFlow, PubSub, BigQuery, and other Google services to meet business requirements.
• Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions.
• Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.
• Ensure Data Quality: Develop and manage batch and real-time data pipelines for Data Warehouse and Datalake to ensure high-quality data delivery.
• Optimize Data Solutions: Collaborate with stakeholders to optimize data solutions and ensure alignment with business objectives.

This position can be performed from anywhere in the United States of America. Required Technical And Professional Expertise

• Google Cloud Ecosystem Expertise: Experience with designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including services such as Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB.
• Data Pipeline Development: Experience in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python.
• Open Source Technologies: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions.
• Data Platform Management: Experience in scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.
• Data Engineering Tools: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem.

Preferred Technical And Professional Experience

• Open Source Proficiency: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions, enabling the development of efficient data pipelines and scalable data architectures.
• Advanced Data Engineering: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem, ensuring seamless integration and data flow.
• Data Platform Optimization: Experience in optimizing data solutions and ensuring alignment with business objectives, collaborating with stakeholders to drive business outcomes through data-driven insights.