Google BigQuery

data warehouse, analytics, google cloud, olap
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Google’s love letter to SQL fans who hate waiting. A serverless, fully-managed data warehouse, BigQuery excels at handling massive datasets without breaking a sweat—or requiring you to think about infrastructure. Its pay-as-you-go pricing model is straightforward enough to make Redshift sweat, and its seamless integration with the Google ecosystem (hello, Sheets and Looker) is a big plus.
But here’s the catch: it’s great for queries, not for transactions. Updates and deletes? Painfully slow. Complex transformations? Better off in a dedicated ETL tool. And while its flat-rate pricing option is tempting for high-volume users, the cost can balloon if you’re not diligent with query optimization or data partitioning.
Best suited for analytics-first shops with structured data and a love of SQL, BigQuery delivers on speed and simplicity—just don’t bring it to a transaction-heavy brawl.

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Jobs in using Google BigQuery for but please no

data engineer

Senior Data Engineer @ veo-technologies

DK | 2025-12-26
Veo’s Senior Data Engineer role promises end-to-end ownership of a data platform underpinning sales, inventory, operations, and customer data at a fast-growing AI-driven sports tech outfit. The... read more »
AI/ML, Data Quality, Analytics, ETL/ELT, Data Engineering, Python, SQL, BigQuery
data engineer

Senior Software Engineer (Enterprise+ App team) @ trustpilot

DK | 2025-12-24
This role is a misrepresentation of what a senior software engineer at Trustpilot should entail. The description is vague, lacks specific technical details, and emphasizes vague notions of... read more »
Management, Data Science, Data Analytics, API, Cloud Computing, React, AWS, AWS Lambda, CloudFormation, TypeScript, DynamoDB, PostgreSQL, BigQuery, Go