OpenNash Jobs
IBM · Bangalore, IND

Data Engineer-Data Platforms-Azure

Bangalore, INDData & Analytics

Listed on IBM’s official careers site. Posted September 4, 2026. Check that it is still open and apply there.

Apply on IBM’s careers site →

Job description

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

As a Data Engineer specializing in Data Platforms on Azure, you will advise on, develop, and maintain data engineering solutions on the Azure Cloud ecosystem. You will design, build, and operate batch and real-time data pipelines using various Azure services.

Your primary responsibilities will include:

Design and Build Data Pipelines

  • Design, build, and operate batch and real-time data pipelines using Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure DataBricks, and Event Hub.

Develop Data Layer

  • Design, build, and operate the data layer on Azure Synapse Analytics, SQL DW, and Cosmos DB.

Implement Azure Data Platform

  • Implement and maintain Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB.

Utilize Open Source Technologies

  • Apply proficiency in open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions.

Ensure Seamless Operations

  • Operate and maintain data engineering solutions to ensure seamless batch and real-time data processing. Required technical and professional expertise

Exposure to Azure Toolset

  • Proficiency in Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure DataBricks, and Event Hub to design, build, and operate batch and real-time data pipelines.

Data Engineering on Azure

  • Experience working with Azure Data Platform components, including ADLS2, Blob Storage, SQLDW, Synapse Analytics with Spark and SQL, Azure functions with Python, Azure Purview, and Cosmos DB.

Open Source Technologies

  • Exposure to open source technologies like Apache Airflow and dbt, Spark / Python, or Spark / Scala to support data engineering solutions.

Data Pipeline Development

  • Experience developing and maintaining data engineering solutions on the Azure Cloud ecosystem, including designing, building, and operating batch and real-time data pipelines.

Azure Data Layer Design

  • Experience designing, building, and operating the data layer on Azure Synapse Analytics, SQL DW, and Cosmos DB. Preferred technical and professional experience

Proficiency in Azure Services

  • Exposure to Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure DataBricks, and Event Hub to design, build, and operate batch and real-time data pipelines.

Data Engineering on Azure

IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

More jobs at IBM

Similar jobs at other companies

OpenNash lists jobs from employers’ own careers sites and does not handle applications. Dates come from the employer where stated; “first seen” and “last checked” are our own observations.