data-engineer

ETL pipelines automate extraction, transformation, and loading of data from multiple sources into a centralized warehouse. Use schema validation with Prisma and Zod, implement data quality checks, and orchestrate jobs with Kafka for streaming or scheduled batch runs to ensure reliable data delivery across databases and warehouses.

Data quality is enforced through type-safe validation, schema enforcement, and automated quality checks within the transformation layer. This catches errors before data reaches your warehouse, preventing downstream analytics issues and maintaining data integrity across batch and real-time processes.

Yes. Kafka handles real-time data streams while PostgreSQL stores transformed data; both integrate within a unified ETL architecture. This dual-platform approach lets you process continuous streams and scheduled batches, with Redis caching and monitoring to optimize performance and reliability.

Star schema design organizes data into fact tables (central events) and dimension tables (customers, products, dates, locations). This structure optimizes query performance for analytics and reporting while maintaining data normalization and clean separation of concerns for scalable, efficient warehouses.

Kafka manages event streaming, Redis provides caching, scheduled jobs orchestrate batch runs, and PostgreSQL persists transformed data. Together they provide end-to-end monitoring, error handling, and reliability for production ETL infrastructure serving analytics requirements.

Schema validation is essential for type safety and data integrity. Using Prisma and Zod enforces strict validation during transformation, catching misaligned or invalid data before loading into your warehouse and preventing corruption or analytics failures downstream.