Google Hiring: Cloud Data Engineer – Bangalore & Hyderabad | Apply Now

Introduction

Google has announced exciting opportunities for professionals looking to build a career in cloud technologies and data engineering. The Google Cloud Data Engineer role offers candidates a chance to work on enterprise-scale data platforms, cloud migration projects, and modern analytics solutions. This position is available in Bangalore and Hyderabad and is ideal for candidates who have experience in cloud platforms, big data technologies, and data processing frameworks.

The Google Cloud Data Engineer position provides exposure to cutting-edge technologies, including Google Cloud Platform (GCP), data warehousing solutions, ETL pipelines, distributed computing frameworks, and large-scale data migration projects. Candidates selected for this role will work with global teams and help organizations accelerate their digital transformation journeys.

Job Overview

Job Summary

The Google Cloud Data Engineer role focuses on designing, building, migrating, and optimizing enterprise-scale data platforms using Google Cloud technologies. Professionals will work closely with customers and stakeholders to understand business requirements and transform them into scalable cloud-based data solutions.

The role involves data migration, infrastructure modernization, data governance implementation, and cloud-native architecture design. Engineers will collaborate with technical teams to deliver high-performance and reliable data systems while ensuring security, scalability, and efficiency.

About Google Cloud Data Engineer Jobs

Google Cloud is one of the world’s leading cloud computing platforms, serving organizations across more than 200 countries and territories. The company provides advanced infrastructure, machine learning capabilities, data analytics services, and enterprise-grade cloud solutions.

The Google Cloud Data Engineer role is designed for professionals who can help organizations modernize their data ecosystems and leverage cloud technologies effectively. Employees gain opportunities to work on large-scale projects involving data warehousing, cloud migration, analytics, machine learning, and distributed computing.

Roles and Responsibilities

• Translate customer requirements into scalable cloud solutions.

• Design and implement cloud-native data architectures.

• Build and operationalize data processing infrastructure.

• Develop data migration strategies for various databases.

• Support migrations involving PostgreSQL, Oracle, AlloyDB, and other databases.

• Create ETL and ELT pipelines for data integration.

• Ensure data quality, reliability, and governance standards.

• Collaborate with stakeholders and technical teams.

• Optimize data processing performance and scalability.

• Support cloud modernization initiatives.

• Implement monitoring and operational best practices.

• Work with distributed data processing frameworks.

• Enable organizations to maximize cloud adoption.

• Deliver enterprise-grade data solutions using Google Cloud Platform.

Required Skills

Technical Skills

• Google Cloud Platform (GCP) expertise for building and managing cloud-based data solutions.

• Strong Python programming skills for scripting, automation, and data processing tasks.

• Java development knowledge for building scalable backend and data applications.

• Scala development experience for working with distributed data systems and Spark-based workloads.

• Hands-on experience with Apache Spark for large-scale data processing and analytics.

• Understanding of the Hadoop ecosystem, including distributed storage and batch processing.

• Data warehousing knowledge for designing efficient analytical data models.

• ETL and ELT development skills for integrating and transforming data from multiple sources.

• SQL proficiency for querying, managing, and optimizing relational databases.

• Database management skills for handling structured and semi-structured data systems.

• Data modeling expertise to design efficient schemas and support business intelligence needs.

• Big data technologies knowledge for processing high-volume and high-velocity datasets.

• Cloud infrastructure understanding for deploying secure and scalable solutions.

• Data migration experience for moving workloads across platforms and environments.

• Information retrieval skills for extracting meaningful insights from large datasets.

• Machine learning fundamentals to support advanced analytics and intelligent data workflows.

• Familiarity with distributed systems and fault-tolerant architectures.

• Knowledge of data pipeline orchestration and workflow automation.

• Experience with monitoring, logging, and troubleshooting cloud data systems.

• Understanding of security, compliance, and access control in cloud environments.

• Ability to optimize performance for storage, compute, and query execution.

• Exposure to modern analytics tools and reporting platforms.

• Familiarity with APIs and data integration methods.

• Experience with version control systems such as Git.

• Knowledge of CI/CD practices for data and cloud deployments.

Soft Skills

• Strong problem-solving ability to identify issues and deliver effective solutions.

• Excellent communication skills to interact with technical and non-technical stakeholders.

• Team collaboration skills for working in cross-functional and global environments.

• Analytical thinking to evaluate data patterns, system behavior, and business needs.

• Stakeholder management skills to align technical delivery with business goals.

• Project coordination abilities to manage tasks, timelines, and deliverables efficiently.

• Adaptability to work in fast-changing cloud and data engineering environments.

• Time management skills to prioritize multiple responsibilities and deadlines.

• Documentation skills for maintaining clear technical records and process notes.

• Customer-focused approach to understand requirements and deliver value-driven solutions.

• Attention to detail for ensuring accuracy in data pipelines and system configurations.

• Learning mindset to stay updated with evolving cloud technologies.

• Collaboration and knowledge-sharing attitude within engineering teams.

• Decision-making skills to support architecture and implementation choices.

• Ownership mindset to take responsibility for project outcomes and quality.

Eligibility Criteria for Google Cloud Data Engineer Jobs

Candidates applying for the Google Cloud Data Engineer role should meet the following requirements:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related discipline.

• Equivalent practical experience may also be considered.

• Experience developing software and data processing algorithms.

• Experience using Python, Java, Scala, Spark, or Hadoop.

• Experience working with public cloud providers, preferably Google Cloud Platform.

• Strong understanding of distributed systems and scalable architectures.

• Ability to work in cross-functional environments.

Preferred Skills

• Strong knowledge of data warehouse architecture and design, including dimensional modeling, star schemas, and analytical reporting structures.

• Hands-on experience with reporting and analytics tools to build dashboards, generate insights, and support business decision-making.

• Deep understanding of big data processing frameworks for handling large-scale, high-volume, and high-speed datasets efficiently.

• Practical experience in data mining and information retrieval to extract patterns, trends, and actionable insights from complex data sources.

• Familiarity with machine learning technologies to support predictive analytics, intelligent automation, and advanced data workflows.

• Working knowledge of NoSQL databases for managing flexible, semi-structured, and unstructured data at scale.

• Experience in MongoDB administration and development, including schema design, indexing, replication, and performance tuning.

• Ability to implement SparkML solutions for building scalable machine learning pipelines on distributed data platforms.

• Understanding of high-availability application design to ensure reliability, fault tolerance, and minimal downtime.

• Knowledge of Infrastructure as Code (IaC) principles for automating infrastructure provisioning and configuration management.

• Practical experience with Terraform automation for creating, updating, and maintaining cloud infrastructure consistently.

• Familiarity with Ansible deployment management for configuration automation, application rollout, and environment standardization.

• Experience with Jenkins CI/CD pipelines for automating build, test, and deployment workflows across cloud environments.

• Knowledge of modern analytical data stores for supporting fast querying, reporting, and large-scale business intelligence use cases.

• Familiarity with transactional database systems and their role in supporting reliable, high-performance operational workloads.

• Exposure to cloud-native monitoring and observability tools for tracking system health, latency, and operational issues.

• Capability to collaborate with architects, developers, analysts, and business teams to deliver end-to-end data solutions.

Google offers highly competitive compensation packages and employee benefits. While the exact salary is not disclosed, selected candidates can expect:

• Attractive annual salary package.

• Performance-based incentives.

• Health and wellness benefits.

• Paid leave and holidays.

• Professional development programs.

• Learning and certification support.

• Career growth opportunities.

• Global exposure.

• Flexible work environment.

• Access to advanced technology platforms.

• Inclusive workplace culture.

Why Apply for Google Cloud Data Engineer Jobs

The Google Cloud Data Engineer role offers numerous advantages for professionals seeking long-term career growth.

Work with Industry-Leading Technologies

Google Cloud provides access to advanced tools, cloud infrastructure, machine learning platforms, and analytics services.

Accelerate Your Career Growth

Employees gain experience working on enterprise-scale projects and modern cloud architectures.

Learn from Top Professionals

Google fosters collaboration among highly skilled engineers, architects, and technology leaders.

Global Impact

The solutions developed by Google Cloud help organizations worldwide transform their businesses.

Continuous Learning

Employees receive opportunities to expand technical expertise through training, certifications, and real-world projects.

Selection Process

1. Online Application Submission

2. Resume Screening

3. Recruiter Discussion

4. Technical Assessment

5. Coding and Data Engineering Interviews

6. Cloud Architecture Evaluation

7. Behavioral Interview

8. Final Hiring Decision

9. Offer Rollout

Important Dates

How to Apply

Candidates can use the application link provided below to submit their applications. Make sure to review the eligibility criteria and complete the application before the deadline.