SEC EDGAR Financials Warehouse
Production-style lakehouse architecture processing SEC financial data with BigQuery, dbt, and automated data quality validation.
Role: Data Engineer•2024
14/14
dbt Tests Passed
100% data quality validation
100%
GE Validations
All data quality checks passing
80-90%
Query Cost Reduction
Via partition/cluster optimization
Daily 06:00 UTC
Automation
Automated refresh schedule
Technology Stack
GCPBigQuerydbtGreat ExpectationsGitHub ActionsLooker StudioPython
Contents
Problem
Financial data analysis requires reliable, up-to-date SEC filing information that's expensive to query and difficult to maintain. Analysts need a fast, cost-effective way to access standardized financial metrics across thousands of companies with guaranteed data quality.
Constraints
- SEC API rate limits requiring careful throttling
- Raw filing data inconsistencies and format variations
- BigQuery costs scaling with data volume scanned
- Need for daily automated updates with zero manual intervention
- Data quality requirements for financial accuracy
Architecture
Built a production-style lakehouse following the medallion architecture pattern:
SEC API → GCS (Raw NDJSON) → BigQuery Raw → dbt Transform → BigQuery Curated → Looker StudioData Flow
- Extract: Rate-limited SEC API calls → GCS storage (NDJSON format)
- Load: Batch loads to BigQuery raw tables with schema validation
- Transform: dbt models creating staging → intermediate → mart layers
- Serve: Curated tables optimized for BI consumption
Results & Impact
Performance Optimization
- Query Cost Reduction: 80-90% reduction in scanned bytes through partition pruning and clustering
- Query Speed: TTM trend queries execute in seconds vs. minutes
- Looker Studio: Dashboard stays responsive with optimized data model
Data Quality
- 100% Validation Success: All dbt tests and Great Expectations checks passing
- Zero Data Issues: Automated quality gates prevent bad data propagation
- Audit Trail: Complete lineage and validation history in BigQuery