Overview
Analytics engineering connects operational sources, Snowflake, SQL transformations and Power BI delivery, supported by AWS and Python data pipelines.
Problem
Analytical consumers need understandable, consistently prepared data rather than disconnected extracts from operational systems.
My role
Integrate data, prepare warehouse-ready datasets and connect engineering workflows to reporting and analytical consumption.
Architecture
The conceptual architecture moves operational data through engineering workflows into Snowflake and an analytical layer consumed by Power BI.
- 01Operational sources
- 02Data engineering
- 03Snowflake
- 04Analytics layer
- 05Power BI
Technical decisions
Separate source integration from analytical preparation. Keep transformations readable and define clear handoffs between the platform and its consumers.
Challenges
Source differences and changing reporting needs require maintainable transformation logic and dependable orchestration.
Data validation
Reconcile prepared datasets against their sources and check structural consistency before downstream reporting.
- Schema
- Row count
- Partitions
- Null values
- Duplicates
- Data types
- Reconciliation
Performance
Review SQL and data access patterns around the analytical workload.
Outcome
The work connects data integration, warehousing and reporting within the broader cloud platform.
Lessons learned
A useful analytics layer is a data engineering outcome: reliable inputs and maintainable transformations support meaningful reporting.