Google BigQuery users list

Email list of companies using Google BigQuery

Q: How many companies use Google BigQuery?

Data InfoMetrix tracks 50,003 companies using Google BigQuery, with 225,077 verified contacts at those accounts as of October 2026. The largest groups are Digital Media, AdTech & Publishing, Retail & Omnichannel E-Commerce and Financial Technology & Neo-Banks. Counts are estimates from our database and market research, so ask us for a live count for your exact criteria.

Q: Who buys and manages Google BigQuery inside a company?

Google BigQuery is usually owned by Google Cloud Data Architecture, Enterprise BI & Chief Data Leadership. Decision-makers include Chief Data Officer (CDO), VP Data Engineering and VP Enterprise Analytics; Lead BigQuery Architect and Principal Analytics Engineer shape the choice, and Data Engineers and Data Analysts use it day to day. Our Google BigQuery list can include any of these roles.

Q: Which Google BigQuery products or editions can I target?

You can target accounts by the Google BigQuery products they run, including BigQuery Editions (Standard, Enterprise, Enterprise Plus), BigQuery Serverless Slots & Autoscaling Commitments, BigQuery ML (In-Warehouse Machine Learning), BigQuery Omni (Multi-Cloud Analytics on AWS/Azure) and BigLake & Iceberg Open Lakehouse Storage. Tell us which edition or module matters and we filter the list to those accounts.

Q: What does Google BigQuery integrate with?

Google BigQuery is most often connected to Looker, dbt Labs, Fivetran, Google Cloud Storage and Pub/Sub, so users of those tools overlap heavily with Google BigQuery accounts. We can filter any list by this kind of stack overlap.

Q: What are the main alternatives to Google BigQuery?

The main alternatives to Google BigQuery are Snowflake Data Cloud, Databricks Lakehouse, Amazon Redshift and ClickHouse Cloud. We also keep verified user lists for Snowflake Data Cloud, Databricks Lakehouse and ClickHouse Cloud, which is useful for competitive displacement campaigns.

Q: What challenges do Google BigQuery customers face?

Common challenges for Google BigQuery accounts include On-Demand Query Bill Spikes vs. Slot Reservation Tradeoffs, Real-Time Streaming Buffer vs. Cold Storage Partition Lag and Multi-Cloud Data Federation & Egress Cost Penalties. Unpartitioned full-table SQL scans in on-demand pricing cause sudden multi-thousand-dollar query bills. Data engineering leads struggle to forecast whether migrating to fixed slot reservations will throttle peak BI queries. These are good angles for outreach to Google BigQuery users.

Q: Can Data InfoMetrix deliver a free sample Google BigQuery contact list before ordering?

Yes. Data InfoMetrix delivers a complimentary 10-record sample tailored to your target Google BigQuery criteria—including target job titles, company size, and region—delivered within one business day with no obligation.

Q: How does Data InfoMetrix guarantee email deliverability for Google BigQuery contacts?

Data InfoMetrix provides a 99% email deliverability guarantee on all Google BigQuery datasets. Every contact record undergoes real-time email verification within a 30 to 45-day refresh cycle, backed by a 1:1 hard bounce replacement policy for 30 days after delivery.

Reach verified Chief Data Officers, VPs of Data Analytics, and Cloud Architects running Google BigQuery. You can filter by slot reservation commitments, BigQuery ML adoption, connected BI tools (Looker/Tableau), and verified direct dials with 99% deliverability.

50,003+Companies

225,077+Contacts

Updated Oct 7, 2026GDPR · CCPA99% accuracy

See sample data

Free Google BigQuery count + 10 sample records

50,003+ companies · 225,077+ contacts available

✓Exact count for your titles, regions and company size
✓10 verified sample records to check quality
✓Price quote within 1 business day

Trusted by B2B and enterprise revenue teams

How many companies use Google BigQuery?

Data InfoMetrix tracks 50,003+ companies running Google BigQuery as of October 2026, with 225,077+ verified contacts at those accounts. The largest groups are Digital Media, AdTech & Publishing, Retail & Omnichannel E-Commerce and Financial Technology & Neo-Banks. Buying decisions usually sit with the Chief Data Officer (CDO), VP Data Engineering and VP Enterprise Analytics. Counts are estimates built from technology footprint scans, job-post signals and partner directories, checked by our research team. How we count

Tracked accounts50,003+

Organizations detected running Google BigQuery.

Verified contacts225,077+

Direct emails and phone dials for decision-makers.

Primary personasChief Data Officer (CDO), VP of Data Engineering, Director of Analytics & Data Products

Full buying committee mapped.

Deliverability SLA99%

SMTP-verified, with 1:1 replacement of any email that hard bounces within 30 days.

Data specification · Updated Q4 2026

Technology category	Data Analytics & Modern Data Stack
Tracked organizations	50,003+ verified accounts worldwide
Decision-maker contacts	225,077+ records with emails, mobile and direct dials
Key roles	Chief Data Officer (CDO), VP of Data Engineering, Director of Analytics & Data Products, platform administrators, IT directors, procurement
Geography	180+ countries · country-by-country counts on request
Verification	DNS and script telemetry, 5-stage SMTP handshake, manual research
Refresh cadence	Continuous, 30–45 day maximum record age at delivery
Compliance	GDPR, CAN-SPAM, CCPA and CASL
Delivery	24–48 hours as CSV, XLSX or CRM API

Editorial & verification

Researched by the Data InfoMetrix research team. Counts updated October 2026.

How we count and verify data

Size your Google BigQuery audience

Pick a region, company size and buying role to estimate how many accounts and contacts you can reach.

Region

Company size

Buying role

Estimated segment✓ 99% SLA

50,003+Target accounts

225,077+Direct contacts

Decision-makers35%

Influencers45%

End users20%

Estimates from our current Google BigQuery dataset. Exact counts come with your quote.

Reach the whole buying committee

A Google BigQuery account is not one contact. We map the people who evaluate, buy, run and use it across Google Cloud Data Architecture, Enterprise BI & Chief Data Leadership.

Decision-makersC-suite & sponsors

Chief Data Officer (CDO)VP Data EngineeringVP Enterprise AnalyticsGoogle Cloud Platform Lead

Influencers & adminsEvaluate & configure

Lead BigQuery ArchitectPrincipal Analytics EngineerData Governance Director

End usersRun it every day

Data EngineersData AnalystsSQL DevelopersBI Engineers

These are examples. We build records for any department, seniority or custom title.

See exactly what you get

Every record carries 60+ fields, from verified email, direct dial and LinkedIn to full postal address, Google BigQuery edition, coexisting stack and intent.

Contact	Buying role	Work email	Direct dial	Company phone	Fax	LinkedIn profile	Company	LinkedIn company page	Industry	Address 1	Address 2	City	State	ZIP code	Country	Google BigQuery footprint	Intent	Status
SPDr. Sanjay PatelChief Data Officer (CDO)	Decision Maker	s.pa**@apexfina******.com	+1 (212) 55*-**	+1 (212) 55*-**	+1 (212) 55*-**	linkedin.com/in/s*ay-pa******	Apex Financial Intelligence Group Inc.	linkedin.com/company/apex-fin-analytics	Financial Services & Investment Research	200 Park Ave 42nd	Fl	New York	NY	10166	United States	Google BigQuery Enterprise Cloud Data Warehouse & Lakehouse	High	✓ Verified 2026
TSTheresa SterlingVP of Data Engineering & Infrastructure	Decision Maker	t.ste**@novac**.io	+1 (415) 55*-**	+1 (415) 55*-**	+1 (415) 55*-**	linkedin.com/in/t*esa-st*******	Nova Cloud Communications Inc.	linkedin.com/company/nova-cloud-tech	Cloud Infrastructure & High-Volume Telemetry	350 Mission St	Ste 1800	San Francisco	CA	94105	United States	Google BigQuery Real-Time Streaming Telemetry & Event Ingestion	High	✓ Verified 2026
MBMaximilian BauerDirector of Business Intelligence & Analytics	Influencer	m.ba**@bavaria****.de	+49 (89) 55*-**	+49 (89) 55*-**	+49 (89) 55*-**	linkedin.com/in/m*ilian-********	Bavaria Digital Retail Group AG	linkedin.com/company/bavaria-retail-ag	Omnichannel Retail & E-Commerce	Maximilianstraße 35	—	Munich	Bavaria	80539	Germany	Google BigQuery Business Intelligence Enterprise Dashboards	Medium	✓ Verified 2026
COClara OswaldLead Analytics Engineer & Data Architect	Practitioner	c.os**@beaconhe*****.uk	+44 (20) 79*-**	+44 (20) 79*-**	+44 (20) 79*-**	linkedin.com/in/c*a-os******	Beacon Clinical Healthcare Analytics Ltd	linkedin.com/company/beacon-clinical-data	Health Analytics & Clinical Research	25 Finsbury Circus	—	London	Greater London	EC2M 7EE	United Kingdom	Google BigQuery Automated Transformation & Semantic Layer	High	✓ Verified 2026
RLRobert LangdonHead of Enterprise Data Governance & Compliance	Decision Maker	r.lan**@prudenti*****.com	+1 (312) 55*-**	+1 (312) 55*-**	+1 (312) 55*-**	linkedin.com/in/r*rt-la*******	Prudential Trust & Bancorp Inc.	linkedin.com/company/prudential-trust-bancorp	Commercial Banking & Wealth Management	111 W Monroe St	Ste 2200	Chicago	IL	60603	United States	Google BigQuery Data Governance, PII Masking & Lineage Tracking	Medium	✓ Verified 2026
NSNadia SolokovaDirector of Machine Learning & Applied AI Data	Influencer	n.sol**@quant***.com	+1 (617) 55*-**	+1 (617) 55*-**	+1 (617) 55*-**	linkedin.com/in/n*a-sol******	Quantum Insurance Risk Modeling LLC	linkedin.com/company/quantum-insurance-data	Property & Casualty Insurance Actuarial Data	100 Cambridge St 14th	Fl	Boston	MA	02114	United States	Google BigQuery Machine Learning Feature Store & Model Training	High	✓ Verified 2026
AMAlain MercierVP of Global Enterprise Architecture	Decision Maker	a.mer**@telecom****.fr	+33 (1) 44*-**	+33 (1) 44*-**	+33 (1) 44*-**	linkedin.com/in/a*n-mer******	Telecom France Networks SA	linkedin.com/company/telecom-france-networks	Telecommunications & Cellular Data Networks	78 Rue de Vaugirard	—	Paris	Île-de-France	75006	France	Google BigQuery Multi-Cloud Data Sharing & Private Clean Rooms	High	✓ Verified 2026
HOHiroshi OgawaSenior Data Platform Solutions Architect	Practitioner	h.og**@tokyodig*****.jp	+81 (3) 55*-**	+81 (3) 55*-**	+81 (3) 55*-**	linkedin.com/in/h*shi-o*******	Tokyo Digital Media Systems KK	linkedin.com/company/tokyo-digital-media	Digital Entertainment & Media Streaming Data	GranTokyo South Tower	32F	Tokyo	Tokyo	100-6632	Japan	Google BigQuery High-Throughput Analytics & Low-Latency SQL	Medium	✓ Verified 2026

Illustrative rows showing 20 of 60+ fields. Full records are unmasked.

We deliver 60+ data fields per record, including intent data. The sample shows a selection only. Tell us which fields you need, including any that aren't listed here, and we'll add them to your list.

Also available:Seniority & departmentJob functionTenure in roleEmployee countAnnual revenueSIC & NAICS codesParent companyWebsite & domainProduct editionContract & renewal timingIntent topics & scoreEmail verification date

How we prove an account runs Google BigQuery

No scraped résumés or stale directories. Three layers confirm a live installation before a contact reaches your list. Read our verification protocol

Pillar 1 · Telemetry

Live DNS & script signals

We inspect response headers, DNS MX and CNAME records and live JavaScript bundles to confirm a running Google BigQuery instance.

DNS · CNAME · MX recordsHTTP headers · JS bundles✓ Verified against live infrastructure

Pillar 2 · Operational intent

90-day hiring signals

We track job requisitions that ask for Google BigQuery skills, which shows ongoing budget and headcount.

Now hiringGoogle BigQuery Administrator

Now hiringGoogle BigQuery Architect

Now hiringGoogle BigQuery Engineer

Pillar 3 · Human & mailbox QA

Pre-delivery SMTP handshake

Each email is tested against the destination mail server, and titles are checked against current company rosters.

SMTP check ✓LinkedIn match ✓Title verified ✓

✓ Deliverable inbox and current role

Deliverability SLA

<3%

Maximum bounce rate, or we replace the contacts. Your sender reputation stays clean.

1:1 hard bounce replacement

Every email that hard-bounces within 30 days of delivery is replaced free, record for record.

24-hour pre-delivery ping

Live SMTP and MX checks right before we send your file.

Instant CRM import

Clean CSV or XLSX, ready for one-click import.

Ready for your outbound stack

Imports straight into the tools you use.

What are you trying to do?

Each campaign needs a different data strategy. Pick your goal to see how we build the audience.

Find companies on rival platforms and reach the people evaluating a move to, or away from, Google BigQuery.

Conquesting playbook

Pitch integrations, add-ons and complementary tools to companies already running Google BigQuery.

Ecosystem playbook

Build account lists around Google BigQuery deployment scale and engage the full committee across departments.

ABM playbook

Conquesting audienceSample

Snowflake Data Cloud

Databricks Lakehouse

Amazon Redshift

→

Google BigQuery

Know the stack around every account

Google BigQuery rarely runs alone. Every record is tagged with the tools detected alongside it, so you can segment by complementary stacks.

dbt

Open the coexistence matrix

What Google BigQuery customers look like

Anonymised profiles of companies in our Google BigQuery dataset of 50,003+ accounts. Named accounts are shared in your free sample, never on a public page.

Google BigQuery account profiles · anonymised

Company size	Industry	Country	Employees	Revenue
SMB	Digital Media, AdTech & Publishing	United States	50–199	$10M–$50M
Enterprise	Retail & Omnichannel E-Commerce	Canada	10,000+	$5B+
Mid-market	Financial Technology & Neo-Banks	Germany	500–999	$100M–$250M
Mid-market	Healthcare Technology & Genomics	United States	200–999	$50M–$250M
Enterprise	High-Growth Cloud SaaS	Australia	5,000+	$1B+
Upper mid-market	Digital Media, AdTech & Publishing	United States	1,000–4,999	$250M–$1B
SMB	Retail & Omnichannel E-Commerce	United States	50–199	$10M–$50M
Enterprise	Financial Technology & Neo-Banks	United Kingdom	10,000+	$5B+

Markets we cover

United States

India

United Kingdom

Canada

Germany

Australia

Singapore

France

New Zealand

Netherlands

Plus 170 more countries. Ask for a country-by-country count of Google BigQuery accounts and we'll include it with your free sample.

Build the list your way

Every list is assembled from source to your product tier, job function, geography and revenue band. Nothing is a fixed export.

Editions & modules

Filter by what they actually run.

+BigQuery Editions (Standard, Enterprise, Enterprise Plus)+BigQuery Serverless Slots & Autoscaling Commitments+BigQuery ML (In-Warehouse Machine Learning)+BigQuery Omni (Multi-Cloud Analytics on AWS/Azure)+BigLake & Iceberg Open Lakehouse Storage

Firmographics

Match revenue, size and industry.

+Industry+$1M–$10B+ revenue+Headcount+Ownership & funding+HQ & branches

Geography

Target any territory.

+US & Canada+US states+UK, DACH, Nordics+APAC & ANZ+Metro clusters

Need multi-attribute segments? Open the Audience Builder

What Google BigQuery teams are struggling with

Know the friction before you reach out, and position your product where it matters.

Friction 1

Runaway Compute Credit Consumption & Warehouse Cost Spikes in Google BigQuery

ImpactEnterprise data teams face unexpected quarterly invoice surges as data volume scales.

OpportunityData warehouse cost optimization, dbt model efficiency auditing, and FinOps for data engineering.

Friction 2

Data Pipeline Failures & Stale Executive Dashboards

ImpactExecutive leadership makes key strategic decisions on broken or outdated metrics due to ETL/ELT pipeline lags.

OpportunityData observability platforms, automated data quality testing, and pipeline alert automation.

Friction 3

Siloed Data Lakes & Strict PII Data Governance Enforcement

ImpactData science & analytics teams wait weeks for secure access to anonymized production datasets.

OpportunityEnterprise data cataloging, automated PII masking, and data access governance tools.

Intent triggers we track

✓Migrating legacy data warehouses to Google BigQuery cloud infrastructure

✓Implementing real-time streaming analytics and dbt transformation pipelines

✓Deploying machine learning models and generative AI workloads

✓Establishing centralized data governance and automated PII masking

“When you talk to companies who already use Snowflake and AWS about a native integration, the conversion rate skyrockets. Data InfoMetrix gave us the exact user list we needed.”

Director of Growth, a data integration companyName withheld at the client's request

Read the 6.8% Response case study

Google BigQuery list questions, answered

How many companies use Google BigQuery?

Data InfoMetrix tracks 50,003 companies using Google BigQuery, with 225,077 verified contacts at those accounts as of October 2026. The largest groups are Digital Media, AdTech & Publishing, Retail & Omnichannel E-Commerce and Financial Technology & Neo-Banks. Counts are estimates from our database and market research, so ask us for a live count for your exact criteria.

Who buys and manages Google BigQuery inside a company?

Google BigQuery is usually owned by Google Cloud Data Architecture, Enterprise BI & Chief Data Leadership. Decision-makers include Chief Data Officer (CDO), VP Data Engineering and VP Enterprise Analytics; Lead BigQuery Architect and Principal Analytics Engineer shape the choice, and Data Engineers and Data Analysts use it day to day. Our Google BigQuery list can include any of these roles.

Which Google BigQuery products or editions can I target?

You can target accounts by the Google BigQuery products they run, including BigQuery Editions (Standard, Enterprise, Enterprise Plus), BigQuery Serverless Slots & Autoscaling Commitments, BigQuery ML (In-Warehouse Machine Learning), BigQuery Omni (Multi-Cloud Analytics on AWS/Azure) and BigLake & Iceberg Open Lakehouse Storage. Tell us which edition or module matters and we filter the list to those accounts.

What does Google BigQuery integrate with?

Google BigQuery is most often connected to Looker, dbt Labs, Fivetran, Google Cloud Storage and Pub/Sub, so users of those tools overlap heavily with Google BigQuery accounts. We can filter any list by this kind of stack overlap.

What are the main alternatives to Google BigQuery?

The main alternatives to Google BigQuery are Snowflake Data Cloud, Databricks Lakehouse, Amazon Redshift and ClickHouse Cloud. We also keep verified user lists for Snowflake Data Cloud, Databricks Lakehouse and ClickHouse Cloud, which is useful for competitive displacement campaigns.

What challenges do Google BigQuery customers face?

Common challenges for Google BigQuery accounts include On-Demand Query Bill Spikes vs. Slot Reservation Tradeoffs, Real-Time Streaming Buffer vs. Cold Storage Partition Lag and Multi-Cloud Data Federation & Egress Cost Penalties. Unpartitioned full-table SQL scans in on-demand pricing cause sudden multi-thousand-dollar query bills. Data engineering leads struggle to forecast whether migrating to fixed slot reservations will throttle peak BI queries. These are good angles for outreach to Google BigQuery users.

Can Data InfoMetrix deliver a free sample Google BigQuery contact list before ordering?

Yes. Data InfoMetrix delivers a complimentary 10-record sample tailored to your target Google BigQuery criteria—including target job titles, company size, and region—delivered within one business day with no obligation.

How does Data InfoMetrix guarantee email deliverability for Google BigQuery contacts?

Data InfoMetrix provides a 99% email deliverability guarantee on all Google BigQuery datasets. Every contact record undergoes real-time email verification within a 30 to 45-day refresh cycle, backed by a 1:1 hard bounce replacement policy for 30 days after delivery.

Sourcing and compliance. Business contacts come from public company sources, professional directories and opt-in publishing partners, then go through SMTP verification and human review. Lists are for B2B outreach: include an unsubscribe link and honour opt-outs (CAN-SPAM), and have a lawful basis such as legitimate interest when you email people in the EU or UK (GDPR). Read how we source and verify data