BigQuery Analytics Expert

Expert in Google BigQuery — SQL analytics at scale, partitioning and clustering strategies, BigQuery ML, materialized views, Dataform pipelines, cost optimization, and data governance.

Gold

v1.0.00 activationsData & AnalyticsTechnologyexpert

SupaScore

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

Decision Usefulness (15%)

8.5

Best for

▸Optimizing BigQuery table partitioning and clustering for 10TB+ datasets to reduce query costs by 60-90%
▸Implementing BigQuery ML models with CREATE MODEL for customer segmentation and demand forecasting
▸Building Dataform data pipelines with dependency management and automated testing
▸Designing materialized views for real-time dashboards while managing BigQuery slot consumption
▸Troubleshooting BigQuery performance bottlenecks in petabyte-scale data warehouses

What you'll get

●Detailed partitioning strategy with DATE partitioning on transaction_date and clustering on [customer_id, product_category] with cost impact analysis showing 75% scan reduction
●Complete BigQuery ML implementation with CREATE MODEL statement for ARIMA_PLUS time series forecasting including feature engineering and evaluation metrics
●Comprehensive Dataform pipeline architecture with SQLX transformations, assertion tests, and incremental table configurations for multi-TB daily processing

Not designed for ↓

×Non-Google Cloud data warehouse platforms like Snowflake or Redshift architecture
×Traditional on-premises ETL tools or database administration outside BigQuery
×Basic SQL query writing without BigQuery-specific optimization considerations
×Cloud infrastructure provisioning beyond BigQuery and related GCP services

Expects

Data architecture details including table sizes, query patterns, current partitioning strategy, slot usage, and specific BigQuery performance or cost optimization challenges.

Returns

Detailed BigQuery optimization recommendations with specific SQL examples, partitioning strategies, cost projections, and implementation steps for scalable analytics architecture.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

bigquerygoogle-cloudsql-analyticsdata-warehousebigquery-mlpartitioningclusteringdataformcost-optimizationdata-governancematerialized-viewsbi-enginestorage-apibigquery-dataframes

Research Foundation: 8 sources (4 official docs, 2 paper, 2 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.