IBM Hiring Data Engineer, Google Data Platforms (Bangalore) | 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 focuses on 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. The role calls for expertise with 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 Bigtable.
• Develop Data Engineering Solutions: Use Google Cloud Storage, Bigtable, BigQuery, DataProc with Spark and Hadoop, and 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 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 BigQuery, Bigtable, and Cloud Spanner for efficient storage and retrieval.

Exposure to Google Cloud services such as DataProc, DataFlow, PubSub, BigQuery, Bigtable, Cloud Spanner, CloudSQL, and AlloyDB is also valued, along with open-source tools like Apache Airflow, dbt, Spark/Python, or Spark/Scala for batch and real-time pipelines.