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Google Bigquery Jobs in Virginia (NOW HIRING)

Deloitte

Google AI Lead Architect

Mclean, VA

$55.75 - $76.50/hr

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement ... Google Professional Machine Learning Engineer certification or the equivalent ML certification.

Strategic Innovation Group LLC

Cloud/AI Engineer

Arlington, VA · Remote

$64.25 - $85.75/hr

Experience leading or executing migrations from on-premises data warehouses to cloud-based data warehouse platforms (e.g., Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse), including data ...

JANSON

Data Engineer (Engineer IV)

Fairfax, VA · On-site

$117K - $140K/yr

Build and maintain data warehouses using platforms like Snowflake, Amazon Redshift, or Google BigQuery. * Optimize storage and query performance for analytical workloads. * Support the Management of ...

Accenture Federal Services

Data Engineer

Arlington, VA · On-site

$93K - $176K/yr

Amazon Redshift, Google BigQuery, Snowflake) * Experience with Python, SQL, Spark, and PySpark * Experience with data platforms like Databricks, Palantir, and Snowflake * Familiarity with data ...

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Google Bigquery information

See Virginia salary details

$24.3K

$108.4K

$174.1K

How much do google bigquery jobs pay per year?

As of Aug 11, 2026, the average yearly pay for google bigquery in Virginia is $108,380.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,618.00 and $145,879.00 per year, depending on experience, location, and employer.

What are jobs in Google Bigquery?

Jobs related to Google BigQuery typically involve managing, analyzing, and optimizing large datasets using SQL and cloud-based tools. Common roles include data analyst, data engineer, and database administrator, often requiring knowledge of cloud platforms, data warehousing, and scripting. These positions may require certifications like Google Cloud Professional Data Engineer and familiarity with BigQuery's features and best practices.

Is Google Bigquery hard to learn?

Google BigQuery is a cloud-based data warehouse that requires understanding SQL and data analysis concepts. While it has a learning curve for beginners, many resources and tutorials are available to help new users become proficient quickly.

What are the key skills and qualifications needed to thrive in the Google BigQuery position?

To excel in a Google BigQuery role, proficiency in SQL, data warehousing concepts, and cloud architecture is essential, often supported by a relevant degree in computer science or data analytics. Familiarity with the Google Cloud Platform (GCP), BigQuery-specific tools, and related certifications like Google Professional Data Engineer are highly valued. Strong analytical thinking, attention to detail, and effective communication skills help professionals distill complex data sets and collaborate across teams. These competencies are vital for efficiently managing, analyzing, and leveraging large-scale data within organizations to drive actionable business insights.

What is a Google BigQuery?

A Google BigQuery job is an asynchronous task used to run queries, load data, export data, or copy data within BigQuery. Each job is assigned a unique job ID and executes in the background, meaning it doesn't require continuous user interaction. There are different types of jobs: query jobs for running SQL queries, load jobs for importing data, export jobs for extracting data, and copy jobs for duplicating tables. Jobs can be managed and monitored through the BigQuery UI, CLI, or API.

What are some typical projects or challenges faced by professionals working with Google BigQuery?

Professionals working with Google BigQuery often tackle projects involving the integration, analysis, and visualization of massive data sets to support business decision-making. Common challenges include optimizing query performance, managing data security and governance, and designing scalable data pipelines to handle rapidly growing information. Day-to-day, you might collaborate with data engineers, data analysts, and business stakeholders to interpret results and translate findings into actionable strategies. This role offers the opportunity to stay at the forefront of cloud data technologies while making a measurable impact on organizational goals.

What are the most commonly searched types of Google Bigquery jobs in Virginia? The most popular types of Google Bigquery jobs in Virginia are:
What are popular job titles related to Google Bigquery jobs in Virginia? For Google Bigquery jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Google Bigquery jobs in Virginia look for? The top searched job categories for Google Bigquery jobs in Virginia are:
Google Bigquery jobs near you
Infographic showing various Google Bigquery job openings in Virginia as of August 2026, with employment types broken down into 1% Internship, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 71% Physical, 4% Hybrid, and 25% Remote job distribution, with an average salary of $108,380 per year, or $52.1 per hour.

Google Cloud Platform (GCP) Data Engineer

Accenture Federal Services

Arlington, VA • On-site

$131K - $158K/yr

Full-time

Posted 21 days ago

Accenture Federal Services rating

8.7

Company rating: 8.7 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

44th of 485 rated business services

Job description

The work you'll do:

  • Design, develop, and maintain robust data pipelines and ETL processes that enable the seamless integration, cleansing, harmonization, and transformations in large-scale data processing systems and data lakes/data warehouses using GCP native services (BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub).
  • Implement performance tuning (partitioning, broadcast joins, caching, shuffle optimization).
  • Integrate data from multiple sources (databases, APIs, cloud storage, potentially streaming).
  • Migrate legacy data stores and ETL pipelines to modernized, cloud-native architectures on GCP.
  • Develop scalable batch and real-time streaming data pipelines using Python, SQL, and Apache Beam.
  • Implement data governance, data quality checks, and secure data access controls in alignment with strict federal security requirements.
  • Collaborate with data scientists, cloud infrastructure engineers, and mission stakeholders to optimize data models and query performance.
  • Design and Implement CI/CD Pipelines: Architect and build robust, automated CI/CD pipelines to streamline the build, test, and deployment processes from development through to production environments

Here's what you'll need:

  • 3 to 5+ years of experience in data engineering, data warehousing, or big data processing.
  • 2+ years of hands-on experience building data pipelines and/or data solutions on Google Cloud Platform (BigQuery, Dataflow, Cloud Composer).
  • Strong programming skills in Python and advanced proficiency in SQL.
  • Experience with ETL/ELT concepts and tools.

Bonus points if you have:

  • Google Cloud Professional Data Engineer Certification.
  • Experience with migrating legacy relational databases (e.g., Oracle, SQL Server) to cloud-native databases (e.g., Cloud SQL, AlloyDB, Spanner).
  • Familiarity with federal data strategies, RMF, and Impact Level 4/5 (IL4/IL5) security and compliance standards.

Security clearance:

  • Active Top Secret (TS) security clearance is required.

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