Data Engineer-Data Platforms-Google - 5

IBM; New York (NY); Remote; USD 140,000 - 180,000; Full time

Job summary

IBM Consulting is seeking a Data Engineer to design, build, and maintain data pipelines on Google Cloud within our hybrid cloud and AI initiatives.

You will work with Google services including DataProc, DataFlow, Pub/Sub, BigQuery, and Airflow to deliver scalable data platforms for clients across industries.

Qualifications

• Design batch and real-time data pipelines for Data Warehouse and Data Lake on Google Cloud.
• Build and maintain data engineering solutions using Google Cloud services.
• Schedule and manage data platforms for efficient pipeline operations.
• Develop data migration solutions ensuring seamless transfers between systems.
• Design and optimize data layer using Google services for storage and retrieval.

Responsibilities

• 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.

Skills

Data pipeline design

Batch & real-time processing

Data migration

Data modeling

Cloud data engineering

Tools

DataProc

DataFlow

Pub/Sub

BigQuery

BigTable

Cloud Spanner

CloudSQL

AlloyDB

Airflow

dbt

Spark

Python

Hadoop

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.

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.

This role can be performed from anywhere in the United States of America

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.