Statistical Abstract Table 3b
Backend Analytics Engineering
Data pipelines and revenue analytics systems
$100M+ revenue tracked
= $5 million
80M+ users analyzed annually
= 2 million users
Designing, building and maintaining the server-side infrastructure behind analytics: ingestion, processing pipelines, storage and real-time reporting.
He architected and maintains an analytics management system that has continuously tracked over $100 million in revenue and analyzed data from over 80 million users annually, with real-time processing, automated reporting and predictive analytics.
Table 3b.1 What the work covers
- Real-time data processing
- High-throughput, low-latency processing with Apache Kafka, Apache Spark and custom stream processing.
- Revenue tracking systems
- Accurate, scalable revenue tracking and financial analytics for enterprise applications.
- Data pipeline architecture
- End-to-end pipelines: ETL and ELT, data validation and automated workflows.
- Analytics API development
- High-performance analytics APIs and microservices as the data access layer.
- Data warehouse design
- Warehouse architecture on Snowflake, BigQuery and custom solutions.
- Performance optimization
- Tuning analytics systems for speed, cost and efficiency.
Table 3b.2 Process
- 1
Requirements analysis
Analytics requirements, data sources and performance targets, plus an assessment of existing systems.
- 2
Architecture design
Data flow architecture, component specifications and scalability planning.
- 3
Infrastructure development
Pipelines, processing systems and storage optimization, built hands-on.
- 4
Integration and testing
Integration with existing systems; validation of data accuracy, performance and reliability.
- 5
Monitoring and maintenance
Ongoing monitoring and optimization to keep the system performing.
Table 3b.3 Technology
| Languages | Python, Node.js, Go, Java, Scala |
|---|---|
| Processing | Apache Spark, Apache Kafka, Apache Airflow, custom streaming |
| Databases and storage | PostgreSQL, MongoDB, Redis, Elasticsearch, ClickHouse, distributed storage |
| Cloud | AWS, Google Cloud Platform, Azure, hybrid cloud |
| Analytics and ML | TensorFlow, PyTorch, scikit-learn, custom ML frameworks |