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Google Cloud Platform Data Analyst Jobs (NOW HIRING)

Decision Six Inc.

Google Cloud Platform Architect

Dearborn, MI · On-site

$59.75 - $76.25/hr

Experience with data management and analytics services in Google Cloud Platform. * Certifications in related technologies or methodologies (e.g., TOGAF, ITIL) are a plus. Qualifications: * Bachelor ...

New

Spiceorb

Data & Cloud FinOps Consultant

Houston, TX · On-site

$55.75 - $76.25/hr

Demonstrated experience with both Google Cloud Platform and AWS cost management . * Strong ... Billing analytics * Forecasting and budgeting * Chargeback/showback * Cost allocation/tagging

Arkhya Tech

Google Cloud Platform Cloud Engineer

Charlotte, NC · On-site

$54.50 - $72.75/hr

- Google Cloud Platform Cloud Engineer, Pre-Onboarding Advisory Location: Charlotte, NC (3 days a ... Capture workload inventory, dependencies, non-functional needs, identity needs, data considerations ...

Google

Customer Engineer, Platform, Google Cloud

Cambridge, MA · On-site

$61 - $81.50/hr

Infrastructure Modernization, Application Modernization, Data Management, Data Analytics, Cloud AI ... About the job The Google Cloud Platform team helps customers transform and build what's next for ...

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Google Cloud Platform Data Analyst information

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How much do google cloud platform data analyst jobs pay per year?

As of Sep 10, 2026, the average yearly pay for google cloud platform data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What does a Google Cloud Platform data analyst do?

A Google Cloud Platform (GCP) Data Analyst is responsible for collecting, processing, and analyzing data using tools and services provided by Google Cloud. They design data pipelines, use BigQuery for querying large datasets, and create visualizations to help organizations make data-driven decisions. Their work often involves collaborating with data engineers, data scientists, and business stakeholders to ensure data accuracy and relevance. Additionally, GCP Data Analysts may optimize queries, ensure data security, and contribute to the migration of data to the cloud.

What are the key skills and qualifications needed to thrive as a Google Cloud Platform data analyst?

To thrive as a Google Cloud Platform Data Analyst, you need strong analytical abilities, expertise in SQL, and a background in data analysis, often supported by a degree in computer science, statistics, or a related field. Proficiency with Google Cloud tools such as BigQuery, Data Studio, and Cloud Storage, as well as certifications like the Google Cloud Professional Data Engineer, is typically required. Excellent problem-solving skills, attention to detail, and the ability to communicate complex insights clearly are key soft skills for this role. These capabilities help drive data-driven decision making and ensure accurate, actionable insights for organizations leveraging cloud-based solutions.

How does a Google Cloud Platform data analyst typically collaborate with data engineers and other team members?

As a Google Cloud Platform Data Analyst, you’ll regularly partner with data engineers to ensure data pipelines are optimized for accurate and timely analysis. You may work closely with business stakeholders to translate their needs into actionable data insights, and often participate in cross-functional meetings to align on project goals. Collaboration tools like BigQuery, Data Studio, and shared dashboards are commonly used to streamline communication and reporting. This teamwork ensures that data-driven decisions are well-supported and aligned with organizational objectives.

What is the difference between Google Cloud Platform Data Analyst vs Data Engineer?

AspectGoogle Cloud Platform Data AnalystData Engineer
Required CertificationsGoogle Cloud Professional Data Engineer, GCP certificationsGoogle Cloud Professional Data Engineer, GCP certifications
Work EnvironmentCloud-based data analysis, reporting, visualizationBuilding data pipelines, data architecture, ETL processes
Employer & Industry UsageTech, finance, healthcare using GCP for analyticsData infrastructure across various industries using GCP

The Google Cloud Platform Data Analyst focuses on analyzing data, creating reports, and visualizations within GCP. In contrast, a Data Engineer designs and maintains data pipelines and infrastructure. Both roles often require GCP certifications and work in cloud environments, but Data Engineers handle the technical architecture, while Data Analysts interpret and present data insights.

What are popular job titles related to Google Cloud Platform Data Analyst jobs?

For Google Cloud Platform Data Analyst jobs, the most frequently searched job titles are:

Infographic showing various Google Cloud Platform Data Analyst job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Google Cloud Platform Architect (Data and AI background)

Philadelphia, PA • On-site

CIS Technologies Inc.
See more jobs at CIS Technologies Inc.external

$64.25 - $82.75/hr

Other

Posted 2 days ago

New

Job description

Role: Google Cloud Platform Cloud Architect (Data and AI background)

Location: Philadelphia, PA Hybrid
Duration: Long Term Contract

Key skills: Claude code, Google Antigravity, openai, Cursor, langchain, Autogen, Vector Databases

Job Description

  • Possess 12-15 years of progressive experience in architecting, designing, and implementing robust data and artificial intelligence solutions, specifically within the Google Cloud Platform ecosystem.
  • This role operates under a hybrid work model, requiring a blend of on-site collaboration and remote work to foster team synergy and project delivery efficiency.

Required Skills:

  • Demonstrated expertise in leveraging Claude code for advanced AI development, with a proven ability to integrate and optimize its capabilities within complex architectures.
  • Mandatory proficiency with Google Antigravity, showcasing a strong capability to design and implement solutions utilizing this critical platform component.
  • Extensive hands-on experience with OpenAI models, including their deployment, fine-tuning, and integration into enterprise-grade applications for diverse use cases.
  • Solid foundational understanding and practical application of Cursor for efficient code generation and development workflows in an AI-centric environment.
  • Proven ability to implement and manage solutions utilizing LangChain, demonstrating expertise in orchestrating complex language model applications and workflows.
  • Strong working knowledge and practical experience with Autogen, enabling the development of multi-agent conversational AI systems and automated task execution.
  • Proficiency in leveraging LlamaIndex for advanced data indexing and retrieval strategies, crucial for enhancing the performance of large language model applications.
  • Demonstrated capability in utilizing Semantic Kernel to build intelligent agents and integrate AI services seamlessly into existing applications and platforms.
  • Foundational understanding of MCP principles, reflecting a broad knowledge base in cloud and enterprise technologies.
  • Expertise in designing and implementing Retrieval Augmented Generation (RAG) architectures to improve the accuracy and relevance of AI model outputs by integrating external knowledge sources.
  • Comprehensive experience with various Vector Databases, including their selection, deployment, and optimization for efficient similarity search and AI data management.

Job Responsibilities

  • Lead the architectural design and implementation of scalable, secure, and high-performance data and AI solutions on Google Cloud Platform (Google Cloud Platform).
  • Drive the strategic vision for leveraging advanced AI/ML capabilities, including large language models and generative AI, within Google Cloud Platform environments.
  • Architect robust data pipelines and machine learning operationalization (MLOps) frameworks to support the full lifecycle of AI models.
  • Guide cross-functional teams in adopting best practices for Google Cloud Platform services, data governance, and AI solution development.
  • Oversee the integration of various AI tools and frameworks, such as Claude code, OpenAI models, LangChain, AutoGen, and LlamaIndex, into enterprise solutions.
  • Define technical standards and patterns for vector databases and Retrieval Augmented Generation (RAG) architectures to enhance AI model performance and relevance.
  • Mentor junior and mid-level architects and engineers, fostering their growth in Google Cloud Platform, data engineering, and AI/ML domains.
  • Ensure the security, compliance, and cost-effectiveness of all Google Cloud Platform-based data and AI infrastructure and applications.
  • Collaborate with product management and business stakeholders to translate complex requirements into actionable technical designs and roadmaps.
  • Evaluate emerging Google Cloud Platform services and AI technologies, recommending strategic adoption to maintain a competitive edge.
  • Establish comprehensive monitoring, logging, and alerting strategies for critical data and AI systems on Google Cloud Platform.
  • Drive continuous improvement initiatives for existing data platforms and AI models, focusing on performance, scalability, and reliability.
  • Manage the technical delivery of complex data and AI projects, ensuring alignment with architectural principles and business objectives.
  • Provide expert guidance on the selection and implementation of appropriate Google Cloud Platform services for data storage, processing, and AI model deployment.
  • Department/Project Description
  • Experience with specific industry verticals (e.g., finance, healthcare).
  • Knowledge of other cloud platforms (AWS, Azure) data and AI services.
  • Experience with MLOps tools and practices.
  • Experience with real-time data streaming and processing.
  • Experience with graph databases and graph analytics.
  • Experience with natural language processing (NLP) and computer vision.

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