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. You will utilize various Google services to develop batch and real-time data pipelines, manage data migration, and design data layers.

Your Primary Responsibilities Will Include:

- Design Data Pipelines: Design and build data pipelines using Google DataProc, DataFlow, PubSub, BigQuery, and other Google services to meet business requirements.
- Develop Data Engineering Solutions: Utilize Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python to develop and manage data engineering solutions.
- Manage Data Platforms: Schedule and manage the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.
- Ensure Data Quality: Develop and manage batch and real-time data pipelines for Data Warehouse and Datalake to ensure high-quality data delivery.
- Optimize Data Solutions: Collaborate with stakeholders to optimize data solutions and ensure alignment with business objectives.

Required Education:

Bachelor's Degree

Preferred Education:

Master's Degree

Required Technical and Professional Expertise:

- Google Cloud Ecosystem Expertise: Experience with 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 in developing and managing batch and real-time data pipelines for Data Warehouse and Datalake using Google Cloud Storage, BigTable, BigQuery DataProc with Spark and Hadoop, and Google DataFlow with Apache Beam or Python.
- Open Source Technologies: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions.
- Data Platform Management: Experience in scheduling and managing the data platform using Google Cloud Scheduler and Cloud Composer (Airflow) to ensure seamless data pipeline operations.
- Data Engineering Tools: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem.

Preferred Technical and Skilled Experience:

- Open Source Proficiency: Experience with open source technologies like Apache Airflow, dbt, Spark/Python, or Spark/Scala in data engineering solutions, enabling the development of efficient data pipelines and scalable data architectures.
- Advanced Data Engineering: Experience with utilizing various data engineering tools and technologies to develop and manage data engineering solutions on Google's Cloud ecosystem, ensuring seamless integration and data flow.
- Data Platform Optimization: Experience in optimizing data solutions and ensuring alignment with business objectives, collaborating with stakeholders to drive business outcomes through data-driven insights.

Years of Experience:8 - 10

📌 Data Engineer-Data Platforms-Google (Bengaluru)
🏢 IBM
📍 Bengaluru