Google Cloud Platform GCP Data Engineer

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

A Data Engineer with expertise in Googles data platforms is responsible for designing, building, and maintaining data engineering solutions on Googles Cloud ecosystem. This includes utilizing services such as Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB for batch and real-time data pipelines, data migration, and data layer design. The Data Engineer should be proficient in using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, Google DataFlow with Apache Beam or Python, and other open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala. Experience in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake, as well as scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), is essential.

Preferred Education

Bachelors Degree

Required Technical And Professional Expertise

GCP Skills (DevOps: Data Services like BigQuery, Cloud SQL, etc. Compute services like Cloud Functions, Cloud Run, Cloud Dataflows, etc., Ops services like Cloud Build, Error Reporting, Trace, Orchestration: PubSub, Cloud Workflows, Cloud Composer, etc.) Python programming language SQL programming language (Generic ANSI and BigQuery specificity with Stored procedure, DML and DDL in mind) Github