Data Engineer-Data Platforms-Google

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.

Required education

Bachelor's Degree

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.

Years of Experience:
5 to 7