Enterprise Data Engineering Platform
Production-grade data engineering platform with multi-source integration, automated ETL pipelines, and comprehensive data quality frameworks processing terabytes of marketing data daily.
The Challenge
Marketing organizations face significant challenges in data integration and processing. Data is fragmented across 15+ different platforms with varying APIs, formats, and schemas. The system needed to process terabytes of data daily, support hundreds of companies with complete data isolation, and ensure 99%+ reliability with automated error recovery.
Multi-Source Integration Complexity
15+ different data sources with varying APIs, authentication methods, data formats, and update frequencies requiring unified integration architecture with standardized interfaces.
Scalability Requirements
Support hundreds of client companies simultaneously while processing terabytes of marketing data daily with isolated data processing and complete data separation.
Data Quality Assurance
Ensure data accuracy, completeness, and consistency across all sources with automated validation, quality checks, and error handling to maintain 99%+ data reliability.
Reliability & Automation
Achieve 99%+ uptime with automated daily processing, comprehensive error handling, recovery mechanisms, and timezone-aware scheduling for optimal resource usage.
Multi-Tenant Architecture
Design scalable multi-tenant system that supports hundreds of companies with complete data isolation, independent processing, and efficient resource utilization.
ETL Pipeline Performance
Process terabytes of data daily within 60 minutes per company for full daily pipeline including data import, merging, transformation, and aggregation.
Our Solution
We developed a comprehensive data engineering platform with modular ETL pipelines, multi-tenant PostgreSQL architecture, and production-grade data quality frameworks. The solution integrates 15+ data sources (Google Ads, Facebook Ads, LinkedIn, Attributy Tracking Platform, BigQuery, Adjust, DV360, and more) through standardized import interfaces, processes terabytes of marketing data daily with automated timezone-aware scheduling, and ensures complete data isolation and quality across hundreds of companies with separate database schemas.
Modular ETL Architecture
Modular, extensible data import architecture with 14+ platform-specific modules, standardized interfaces, and configuration-driven API endpoints enabling rapid integration of new data sources.
Multi-Tenant Database Design
Scalable PostgreSQL database design with company-specific schemas, complete data isolation, automated schema creation, and efficient connection pooling supporting hundreds of companies.
Automated Processing Workflows
Production-grade ETL pipelines with timezone-aware scheduling (1 AM per company timezone), automated daily processing, comprehensive error handling, and status tracking.
Data Quality Frameworks
Comprehensive data validation with automated quality checks, completeness validation, report quality verification, and company status management (Complete, Running, Error) for monitoring.
Data Engineering Excellence
Our data engineering team built a production-grade data platform that demonstrates exceptional technical sophistication in ETL pipeline development, database architecture, and data operations. We engineered modular data import systems, scalable multi-tenant PostgreSQL schemas, automated data processing workflows, and comprehensive data quality validation frameworks that handle 15+ diverse data sources with reliability and efficiency.
Multi-Source Data Integration
Comprehensive ETL pipelines integrating 15+ data sources including Google Ads, Facebook Ads, LinkedIn, Attributy Tracking Platform, BigQuery, Adjust, DV360, and more with standardized interfaces.
Modular import architecture with 14+ platform-specific modules
Standardized data normalization and schema mapping
Incremental data loading with timestamp-based updates
Comprehensive error handling, retry logic, and rate limit management
Multi-Tenant Database Architecture
Scalable PostgreSQL database design with isolated schemas per company, supporting hundreds of clients with complete data separation.
Company-specific PostgreSQL schemas with automated creation
Complete data isolation between clients (zero data leakage)
Efficient connection pooling and transaction management
Horizontal scaling capability with stateless processing
Automated ETL Processing
Production-grade ETL pipelines with timezone-aware scheduling, automated daily processing, and comprehensive error recovery mechanisms.
Timezone-aware scheduling (1 AM per company timezone)
Automated daily data import and processing workflows
Data merging, transformation, and aggregation pipelines
Error handling with email/Slack notifications and status tracking
Data Quality & Validation
Comprehensive data validation frameworks with automated quality checks, completeness validation, and governance policies.
Automated data completeness and quality validation
Report quality checks after processing completion
Table existence and schema validation before operations
Company status management (Complete, Running, Error) for monitoring
ETL Pipeline Architecture
Our modular ETL pipeline architecture processes terabytes of marketing data daily through standardized stages: Extract from 15+ sources (web analytics, ad platforms, mobile analytics, offline media) via API integrations and file imports, Transform with schema normalization, data type conversion, duplicate detection, and timezone normalization, and Load into multi-tenant PostgreSQL schemas using bulk insert operations and transaction management. The system handles incremental updates, bulk operations, connection pooling, and ensures data integrity across all stages.
Extract
15+ platform integrations with standardized interfaces, API calls with error handling and retries, incremental data loading, and CSV file parsing with schema validation.
Transform
Schema normalization across sources, data type conversions and validation, missing value handling, duplicate detection and removal, and timezone normalization.
Load
Multi-tenant PostgreSQL with isolated schemas, bulk insert operations for performance, transaction management for data integrity, and upsert logic for incremental updates.
Impact & Results
00+
Data sources integrated
00%+
Data pipeline reliability
00%+
Reduction in manual data processing
00/7
Automated data processing
<00min
Daily processing time per company
000+
Companies supported
Operational Efficiency
80%+ reduction in manual data processing through automated ETL pipelines. Real-time data availability provides immediate insights for decision-making with automated daily processing at 1 AM company timezone.
Data Quality
Comprehensive data validation ensures 99%+ data completeness and accuracy. Automated quality checks catch issues before they impact downstream systems with report quality verification and completeness validation.
System Reliability
99%+ pipeline uptime with comprehensive error handling and automated recovery. Processing completed within 60 minutes per company for full daily pipeline including data import, merging, transformation, and aggregation.
Scalability & Multi-Tenancy
Multi-tenant PostgreSQL architecture supports hundreds of companies with isolated schemas, independent processing, and parallel processing. Timezone-aware scheduling optimizes resource usage with horizontal scaling capability for future growth.
Technology Stack
We leveraged modern data engineering technologies, database systems, and cloud infrastructure to build a scalable, production-grade data platform.
Python
PostgreSQL
AWS
FastAPI
pandas
NumPy
Key Technical Achievements
Modular Architecture
14+ independent import modules with clear separation of concerns, reusable components, and dependency injection for maintainability and extensibility.
ETL Pipeline
Automated data processing with timezone-aware scheduling, incremental updates, bulk operations, and comprehensive error handling with recovery mechanisms.
Data Integration
15+ platform integrations (Google Ads, Facebook, LinkedIn, TikTok, Snapchat, BigQuery, Attributy Tracking Platform, Adjust, DV360) with standardized interfaces, error handling, and data quality validation.
Data Quality
Comprehensive validation and quality frameworks with automated completeness checks, report quality verification, and company status management for monitoring.