Data Engineer-Data Platforms-Google

IBM; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

IBM Consulting seeks a Data Engineer specializing in Google Cloud to design, build, and maintain data engineering solutions on Google’s Cloud ecosystem. You will work on batch and real-time pipelines, data migrations, and data layer design using services like DataProc, DataFlow, Pub/Sub, BigQuery, and BigTable.

Responsibilities include scheduling and managing data platforms with Cloud Scheduler and Airflow, and optimizing the data layer for efficient storage and retrieval.

Qualifications

• Experience designing, building and maintaining data pipelines on Google Cloud Platform.

Responsibilities

• Design Data Pipelines: batch and real-time pipelines for Data Warehouse and Datalake using Google services (DataProc, DataFlow, Pub/Sub, BigQuery, BigTable).
• Develop Data Engineering Solutions: use Google Cloud Storage, BigTable, BigQuery DataProc with Spark/Hadoop, and DataFlow with Apache Beam or Python.
• Manage Data Platforms: schedule and manage data platforms with Google Cloud Scheduler and Cloud Composer (Airflow).
• Implement Data Migration: develop data migration solutions using Google services.
• Optimize Data Layer: design and optimize data layer with BigQuery, BigTable, Cloud Spanner.

Skills

Google Cloud Platform

Data Pipelines

Spark/Python

Apache Airflow

BigQuery

BigTable

Cloud Spanner

DataProc

Pub/Sub

Hadoop

Python

Education

Master's Degree

Tools

DataProc

DataFlow

Pub/Sub

BigQuery

BigTable

Cloud Spanner

CloudSQL

AlloyDB

Airflow

dbt

Spark

Hadoop

Python

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 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.