Data Engineer-Data Platforms-Google

Introduction

A career in IBM Consulting is grounded in enduring client relationships and close global collaboration. You’ll partner with leading organizations across industries, supporting their hybrid cloud and AI initiatives. With guidance from strategic partners, access to IBM technology, and Red Hat, you’ll gain the resources needed to drive meaningful change and help clients realize faster, measurable impact. At IBM Consulting, curiosity is a catalyst for success—you’ll be encouraged to question the status quo, pursue new ideas, and build inventive solutions that produce real-world results. Our growth and empathy culture supports your long-term career development, while recognizing and valuing the distinctive skills and experiences you bring.

Your Role And Responsibilities

As a Data Engineer focused on Google’s data platforms, you will design, develop, and maintain data engineering solutions within the Google Cloud environment. The position calls for strong knowledge of using multiple Google services to support both batch and real-time data pipelines, data migration, and data layer design.

Your Primary Responsibilities Will Include

• Design Data Pipelines: Design and build batch and real-time data pipelines for a Data Warehouse and Datalake using Google services such as DataProc, DataFlow, PubSub, BigQuery, and Big Table.
• Develop Data Engineering Solutions: Use Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to create, operate, and maintain data engineering solutions.
• Manage Data Platforms: Plan, schedule, and maintain the data platform with Google Cloud Scheduler and Cloud Composer (Airflow), supporting reliable and efficient data pipeline execution.
• Implement Data Migration: Create and implement data migration solutions using Google services to enable smooth data transfer between systems.
• Optimize Data Layer: Build and improve the data layer using Google services such as BigQuery, Big Table, and Cloud Spanner to support efficient data storage and retrieval.
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Google Cloud Ecosystem Expertise: Exposure to 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: Exposure to developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google services and open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala.
• Google Cloud Services Proficiency: Experience working with 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.
• Data Platform Management Knowledge: Exposure to scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) for efficient data pipeline operations.
• Data Layer Design Understanding: Experience working with data layer design using Google services such as BigQuery, Big Table, and Cloud Spanner for efficient data storage and retrieval.
  
  

Preferred Technical And Professional Experience

• Open Source Technologies: Exposure to utilizing open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala for developing and managing batch and real-time data pipelines.
• Data Migration Solutions: Experience working with Google services to develop and implement data migration solutions, ensuring seamless data transfer between systems.
• Cloud Composer Expertise: Exposure to using Cloud Composer (Airflow) for scheduling and managing the data platform, ensuring efficient data pipeline operations.