Data Platform and Pipeline Engineering
Reliable reporting depends on the platform beneath it. We build the ingestion, storage, and transformation layers with explicit handling for late, missing, and malformed data rather than assuming clean inputs.
Key Areas
- Data warehouse and data lake design for analytical workloads
- Ingestion pipelines from internal systems, third-party platforms, and files
- Transformation logic with version control and documented lineage
- Data quality validation with defined handling for failed records
- Incremental and historical load strategies
- Scheduling, orchestration, and pipeline failure alerting
Business Intelligence and Decision Support
Dashboards are built around the decisions they inform, so numbers mean the same thing across reports. Access and definitions are governed to prevent the same metric being calculated two different ways.
Key Areas
- Dashboard and report development in established BI platforms
- Metric definitions documented and applied consistently across reports
- Self-service models that let teams answer questions without engineering
- Role-based access control over sensitive datasets
- Near real-time reporting where operational decisions depend on it
- User training and documentation so reports are interpreted correctly