IBM Hiring Data Engineer Google Data Platforms (Gurgaon) | Hybrid

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You will work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from strategic partners, IBM technology, and Red Hat, you will have the tools to drive meaningful change and accelerate client impact.

At IBM Consulting, curiosity fuels success. You will be encouraged to challenge the norm, explore new ideas, and build innovative solutions that deliver real results. The culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Role Overview

As a Data Engineer specializing in Google's data platforms, you will design, build, and maintain data engineering solutions on Google's Cloud ecosystem. The role calls for expertise in using various Google services for batch and real-time data pipelines, data migration, and data layer design.

Key Responsibilities

• 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: Use Google Cloud Storage, BigTable, BigQuery, DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to build and maintain solutions.
• Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow), keeping pipeline operations efficient.
• 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 BigQuery, Big Table, and Cloud Spanner, ensuring efficient storage and retrieval.
• Google Cloud Ecosystem Expertise: Exposure to designing, building, and maintaining solutions on Google's Cloud ecosystem, including DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB.
• Data Pipeline Development: Exposure to developing and managing batch and real-time pipelines for Data Warehouse and Datalake using Google services and open-source tools such as Apache Airflow, dbt, Spark/Python, or Spark/Scala.
• Data Platform Management: Exposure to scheduling and managing the data platform with Google Cloud Scheduler and Cloud Composer (Airflow) for efficient pipeline operations.
• Data Layer Design: Experience working with data layer design using BigQuery, Big Table, and Cloud Spanner for efficient storage and retrieval.
• 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, keeping pipeline operations efficient.