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IBM

Data Engineer-Data Platforms-Google

IBM

Published 09 Apr 2026
Gurgaon, India
Full Time

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Role Highlights

Languages used

Python
SCALA

Key skills

Data Engineer
Open Source
Data Migration
Cloud
AI
Batch
DataFlow
Storage
Operations

Tools, Libraries and Frameworks

GCP
IOS
Red Hat
IBM
BigQuery
Apache
Composer
Airflow
Hadoop

Description

The professional designs, builds, and maintains data engineering solutions within the Google Cloud ecosystem. Responsibilities include developing batch and real-time data pipelines for data warehouses and data lakes. The role involves managing data platforms to ensure efficient pipeline operations and implementing data migration solutions for seamless system transfers. Additionally, the individual optimizes data layers to facilitate efficient storage and retrieval processes.

Required Qualifications and Skills

The role requires expertise in the Google Cloud ecosystem, specifically regarding the design and maintenance of data engineering solutions. Candidates must possess experience in developing batch and real-time data pipelines using services such as DataProc, DataFlow, and BigQuery. Proficiency in open-source technologies like Apache Airflow, dbt, Spark, and Python or Scala is necessary for managing data platforms. No specific degree or professional qualification requirements are mentioned in the text.

Disclaimer

Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.

About the company

IBM

Size

305978

Website

ibm.com

HQ

Armonk, New York, US

Public/Private

Public Company

Description

IBM infuses core business operations with intelligence, from machine learning to generative AI, to make organizations more responsive, productive, and resilient. It helps clients put AI into action now, creating real value with trust, speed, and confidence across various areas like digital labor, IT automation, and security. The ability to utilize all data is critical, as AI's effectiveness is dependent on the quality of data fueling it, with IBM's AI, and data platform aiming to scale and accelerate AI's impact with trusted data. IBM's hybrid cloud platform offers a comprehensive approach to development, security, and operations across hybrid environments, laying a flexible foundation for leveraging data wherever it resides.

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