01 / Inside the engagement
Situation
An analytics warehouse must keep working as files arrive, source records change and new reporting needs emerge. The client needed engineers who could investigate problems, update the data-processing logic and carry those changes through validation.
Context
- Source changes
- File deliveries
- Warehouse needs
UTIS contribution
- SQL + SSIS updates
- Data-load checks
- Monitoring component
Simplified workflow. The case text describes the contribution, delivery stage and evidence.
Explore the full conceptual diagram
02 / Inside the engagement
What UTIS did
- Developed and supported SQL Server warehouse processing and SSIS data loads from Amazon S3.
- Refined source matching and duplicate-record handling, with production fixes for changed source records.
- Automated historical file-loading work and continued report and warehouse development alongside day-to-day support.
- Delivered an AWS S3 monitoring component using C# and AWS CDK, with the client confirming its deployment.
03 / Inside the engagement
Results
- Work records document production processing fixes, data-load automation and validation activity.
- The client confirmed deployment of the S3 monitoring component.
- The engagement combined ongoing warehouse engineering with operational support as priorities changed.
Why it matters to you
Analytics reliability depends on the less visible work between a source file and a report: matching records, handling change, monitoring delivery and validating fixes. Owning those details gives the client a practical engineering resource for its evolving data estate.
Stack
Evidence basis: engineering delivery reports, client approval of work reports and a client deployment confirmation for the monitoring component. No uptime, billing-savings or end-to-end automation result is claimed.