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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 Google's data platforms, you will design, build, and maintain data engineering solutions on Google's Cloud ecosystem. This role requires expertise in utilizing various Google services for batch and real-time data pipelines, data migration, and data layer design. Your primary responsibilities will include: 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: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google 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 data 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 Google services such as BigQuery, Big Table, and Cloud Spanner, ensuring efficient data storage and retrieval. Preferred Education Master's Degree Required Technical And Professional Expertise Google Cloud Ecosystem Expertise: Exposure to designing, building, and maintaining data engineering solutions on Google's Cloud ecosystem, including services such as Google DataProc, DataFlow, PubSub, BigQuery, Big Table, Cloud Spanner, CloudSQL, and AlloyDB. Data Pipeline Development Experience: Exposure to developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google services and open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala. Google Cloud Services Proficiency: Experience working with Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to build and maintain data engineering solutions. Data Platform Management Knowledge: Exposure to scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) for efficient data pipeline operations. Data Layer Design Understanding: Experience working with data layer design using Google services such as BigQuery, Big Table, and Cloud Spanner for efficient data storage and retrieval. Preferred Technical And Professional Experience Open Source Technologies: Exposure to utilizing open-source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala for developing and managing batch and real-time data pipelines. 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, ensuring efficient data pipeline operations, Git, Terraform and Python API implementation.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.