Data processing, validation and reconciliation across business systems
Enterprise Data Engineering & Integration
Developed data-processing and integration workflows that support business systems, data validation, record reconciliation and cross-platform operations.
- Context
- Professional · National Sales Partners
- Role
- Built the transformation and validation workflows
- Period
- 2025 — Present
- Stack
- Python, SQL, MySQL, pandas, REST APIs
This is work for my employer. Its design, data and the business it supports are confidential, so this page covers my role, the engineering domains and the technologies involved, not how the system works. I'm happy to talk through the engineering in an interview, within those limits.
My role
- Write Python and SQL workflows that move and transform records between business platforms.
- Map fields between systems that model the same data differently, and preserve source identifiers for traceability.
- Detect duplicates and conflicts before anything is written, and report exceptions for review.
- Analyze large, multi-million-row datasets with SQL to investigate issues and confirm results.
Domains & stack
- ETL
- Data validation
- Record reconciliation
- SQL analysis
- API synchronization
- Exception reporting
- Python
- SQL
- MySQL
- pandas
- REST APIs
How I approach this kind of work
Right before fast
Counts, totals and spot checks reconcile source and target before results are trusted.
Exceptions are output
Records that don't fit are reported and reviewed, never silently dropped.
Re-runnable by design
Running a batch twice must not create anything twice.