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Remote Google Bigquery Jobs (NOW HIRING)

Collectorsuniverse

Staff Data Engineer

For remote roles, and at our discretion, candidates may be asked to participate in an on-site ... Own the architecture of our data platform, spanning ingestion (Hevo, Estuary), our Google BigQuery ...

SWITS DIGITAL Private Limited

GCP Data Engineer

$117K - $140K/yr

Denver, CO (Remote) Only W2 Candidates Job Summary We are seeking a highly skilled Data Engineer ... Strong hands-on experience with Google Cloud Platform (GCP) * Expertise in BigQuery (partitioning ...

SWITS DIGITAL Private Limited

GCP Data Engineer

$117K - $140K/yr

Denver, CO (Remote) Only W2 Candidates Mandatory Skills - GCP - Java - Medalion Job Summary We are ... Strong hands-on experience with Google Cloud Platform (GCP) * Expertise in BigQuery (partitioning ...

Whitespace

Software Engineer-AFRL

Alexandria, VA · On-site +1

$131K - $172K/yr

Remote / Alexandria, VA Clearance: Active TS/SCI or eligibility to be cleared Are you ready to be ... Experience with Big Data processing tools, such as Spark/Hadoop, Google's BigQuery, AWS's Athena ...

Whitespace

Software Engineer-AFRL

Alexandria, VA · Remote

$125K - $165K/yr

Remote / Alexandria, VA Clearance: Active TS/SCI or eligibility to be cleared Are you ready to be ... Experience with Big Data processing tools, such as Spark/Hadoop, Google's BigQuery, AWS's Athena ...

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Remote Google Bigquery information

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How much do remote google bigquery jobs pay per hour?

As of Sep 2, 2026, the average hourly pay for remote google bigquery in the United States is $27.67, according to ZipRecruiter salary data. Most workers in this role earn between $21.63 and $33.17 per hour, depending on experience, location, and employer.

What is a remote Google BigQuery?

A Remote Google BigQuery job is a position where professionals manage, analyze, and optimize large datasets using Google BigQuery, a fully-managed cloud data warehouse, while working from a location outside of a traditional office. These roles typically involve writing SQL queries, building data pipelines, and collaborating with data engineers and analysts to derive insights from data stored in the cloud. Remote BigQuery specialists may also be responsible for maintaining data security, optimizing query performance, and integrating BigQuery with other data tools. This flexible setup allows employees to work from anywhere with a stable internet connection while supporting organizations' data needs.

What are the key skills and qualifications needed to thrive as a remote Google BigQuery specialist?

To excel as a Remote Google BigQuery Specialist, you need a solid background in SQL, data warehousing concepts, and experience with cloud-based analytics platforms, typically supported by a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP), BigQuery ML, data visualization tools like Looker or Tableau, and relevant certifications such as Google Professional Data Engineer are highly beneficial. Strong problem-solving skills, attention to detail, and effective remote communication set top performers apart. These skills and qualifications enable efficient management of large datasets, insightful analytics, and seamless collaboration in distributed teams.

What are some common challenges faced by professionals working remotely with Google BigQuery, and how can they be addressed?

One common challenge remote Google BigQuery professionals face is optimizing query performance while managing cost, since inefficient queries can quickly increase expenses. Collaboration with distributed teams can also be tricky, especially when aligning on data schema changes or troubleshooting issues across time zones. To address these, it's helpful to establish clear documentation practices, use version control for SQL scripts, and schedule regular check-ins with team members. Leveraging Google BigQuery's built-in monitoring and cost control tools also helps maintain project efficiency and budget constraints.

What is the difference between Remote Google Bigquery vs Remote Data Analyst?

AspectRemote Google BigqueryRemote Data Analyst
Required CredentialsSQL, Cloud certifications, Google Cloud certificationsSQL, Data analysis, Excel, sometimes certifications
Work EnvironmentCloud platforms, data warehouses, remote teamsData visualization tools, spreadsheets, reporting platforms
Industry UsageData engineering, cloud services, analyticsBusiness intelligence, reporting, insights

Remote Google Bigquery specialists focus on managing and querying large datasets using Google Cloud, requiring technical skills and cloud certifications. Remote Data Analysts interpret data, create reports, and provide insights, often using visualization tools. While both roles work remotely and handle data, Bigquery roles are more technical and cloud-focused, whereas Data Analysts focus on analysis and reporting.

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Remote Google Bigquery jobs near you
Infographic showing various Remote Google Bigquery job openings in the United States as of August 2026, with employment types broken down into 77% Full Time, 8% Part Time, and 15% Contract. Highlights an 100% Remote job distribution, with an average salary of $57,562 per year, or $27.7 per hour.

Data Engineer(Java+Python+GCP)

SWITS DIGITAL Private Limited

Remote

$117K - $140K/yr

Full-time

Re-posted 18 days ago

Job description

Title: Data Engineer - GCP
Location: Denver, CO (Remote)
Job Summary
The client is seeking a highly skilled Data Engineer with deep expertise in Google Cloud Platform (GCP) and modern data architecture. The ideal candidate will have hands-on experience designing scalable data pipelines, implementing Medallion Architecture, and building robust enterprise-grade data solutions.
This role requires strong technical proficiency in BigQuery, PySpark, Dataflow, and Airflow, along with a solid understanding of cloud data governance, performance optimization, and CI/CD practices.
Key Responsibilities
  • Design, develop, and maintain scalable batch and real-time data pipelines on GCP
  • Implement and manage Medallion Architecture (Bronze, Silver, Gold layers) for data processing
  • Build high-performance data transformations using Python and PySpark
  • Develop and optimize complex SQL queries for analytical workloads
  • Work extensively with BigQuery for large-scale data processing and performance tuning
  • Develop and deploy pipelines using Cloud Dataflow
  • Orchestrate workflows using Cloud Composer (Apache Airflow)
  • Manage data storage and lifecycle using Google Cloud Storage (GCS)
  • Implement version control and CI/CD pipelines using Git-based tools
  • Ensure data security, governance, and access control using GCP IAM
  • Optimize data solutions for performance, scalability, reliability, and cost-efficiency

Required Skills & Experience
  • Strong hands-on experience with Google Cloud Platform (GCP)
  • Expertise in BigQuery (partitioning, clustering, query optimization)
  • Proven experience implementing Medallion Data Architecture
  • Strong programming skills in Python and PySpark
  • Advanced proficiency in SQL (complex joins, window functions, performance tuning)
  • Hands-on experience with Cloud Dataflow
  • Experience with Cloud Composer (Airflow) for orchestration
  • Experience working with Google Cloud Storage (GCS)
  • Knowledge of version control systems (Git) and CI/CD practices
  • Strong understanding of GCP IAM and cloud security best practices

Preferred Qualifications
  • Experience working with large-scale enterprise data platforms
  • Knowledge of data warehousing and data lake concepts
  • Familiarity with real-time streaming frameworks
  • Experience in data governance and data quality frameworks
  • Exposure to Agile/Scrum methodologies

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