Financial Data Automation Platform
Founder's prior work, generalized
Automated financial data workflows connecting external data services, internal databases and operational systems to reduce repetitive processing and improve data consistency.
This case study has been intentionally generalized to protect confidential business information.
Overview
A set of automated workflows that move financial data between external data services, internal databases and the operational systems that depend on them.
The problem
Recurring financial data work involved repetitive processing across several systems. Done by hand, that kind of work is slow, and the same data can end up inconsistent from one system to the next.
The solution
Scheduled, database-driven workflows that retrieve, transform, check and deliver the data without manual steps.
- Integration with external data services through their APIs.
- SQL Server processes that transform incoming data into the shape internal systems expect.
- Validation of the data before it is used downstream.
- Scheduled automation, so the workflows run unattended.
- Exception handling that separates records needing attention from the ones that processed cleanly.
- Reconciliation and operational reporting on the results.
Architecture
- External data servicesAccessed through APIs
- Scheduled jobsRetrieval and orchestration scripts
- SQL Server processingTransformation, validation and reconciliation
- Operational systemsDownstream processes and reports
Key engineering decisions
Validate before data moves downstream
Checks run as part of the workflow, before data reaches the systems that rely on it, rather than after a problem has already spread.
Business rules live in maintainable database logic
Financial rules are expressed in SQL Server processes that can be read, tested and changed, rather than in one-off manual steps.
Exceptions are surfaced, not skipped
Records that fail a check are routed to exception handling so they can be investigated, instead of disappearing silently from an unattended run.
Challenges
Depending on external services
Workflows rely on data feeds and APIs outside the organisation’s control, so they have to cope with services that are unavailable or return incomplete data.
Keeping several systems consistent
The same data is used in more than one place, which is what makes reconciliation a core part of the workflow rather than an afterthought.
Diagnosing unattended jobs
When a scheduled process misbehaves, finding the cause means tracing it across APIs, data feeds, database processes and third-party systems.
Reliability & security
Validation
Incoming data is checked before it is processed further.
Exception handling
Failures are captured and routed for follow-up rather than ignored.
Reconciliation
Results are compared across systems to confirm the data agrees.
Production support
Scheduled jobs are supported in production, including troubleshooting when an external service or data feed changes.
Results & impact
- Recurring processing runs as automated, scheduled workflows rather than repetitive manual steps.
- Validation and reconciliation make the data that operational systems rely on more consistent.