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IBM

Data Engineer-Data Platforms-Azure

IBM

Published 17 May 2026
Bangalore, India
Full Time

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Role Highlights

Languages used

SQL
Python
SCALA

Key skills

Data Engineer
Open Source
Data Processing
Cloud
AI
Batch
Database
Storage
Operations

Tools, Libraries and Frameworks

Data Factory
Azure Databricks
IOS
Red Hat
IBM
DataBricks
Apache
Airflow

Description

The role involves advising on, developing, and maintaining data engineering solutions within the Azure Cloud ecosystem. The individual will design, build, and operate both batch and real-time data pipelines using various Azure services. Responsibilities include developing the data layer on platforms such as Azure Synapse Analytics, SQL DW, and Cosmos DB. The position requires implementing and maintaining various Azure Data Platform components. Additionally, the role involves utilizing open-source technologies to support data engineering solutions and ensuring the seamless operation of data processing systems.

Required Qualifications and Skills

The role requires proficiency in the Azure toolset, specifically regarding the design and operation of batch and real-time data pipelines. Candidates must have experience with Azure Data Platform components, including ADLS2, Blob Storage, SQL DW, Synapse Analytics, Azure functions, Azure Purview, and Cosmos DB. Furthermore, expertise in open-source technologies such as Apache Airflow, dbt, Spark, Python, or Scala is necessary. No specific degree or professional qualifications 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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