The Brief
JFIntech builds SaaS tooling for financial services operations — the category of software that sits between the core banking system and the customer-facing products. Reconciliation, reporting, regulatory filing preparation, exception management.
The engineering challenge was not product invention but product stability and scale. The system was growing faster than its architecture could accommodate. Production incidents were frequent, root cause analysis was slow, and the team was spending more time firefighting than building.
The mandate: stabilize the platform, instrument it properly, and restore the team's ability to ship features confidently.
The Approach
Production incidents in this context were almost entirely caused by two things: synchronous operations that should have been asynchronous, and insufficient observability that made debugging slow and root cause identification unreliable.
Fintech systems handle regulated data. This shapes the architecture: auditability, data integrity, and failure isolation matter more than raw throughput. The migration from a synchronous, monolithic processing model to an async, event-driven model had to be done carefully — not because async is complicated, but because financial data cannot be silently dropped or duplicated during a migration.
"In financial systems, a silent failure is worse than a loud one. Design for noise."
The Build
The first month was observability. Before changing any production code, the system needed enough instrumentation to understand what was actually happening. Structured logging (JSON format, consistent field names across services), distributed tracing (OpenTelemetry), and the right set of metrics for a fintech workload: transaction processing latency, error rates by transaction type, queue depths, and database connection pool saturation.
The dashboards that resulted from this work immediately revealed the bottlenecks. Three synchronous database operations, called in sequence in the reconciliation flow, were responsible for 60% of the timeout-related incidents. They were not slow individually; they were slow because they were serialized when they could have been parallelized, and because connection pool exhaustion under load caused cascading timeouts.
The async refactor addressed both: the serialized operations were parallelized using asyncio.gather, and the connection pool was sized correctly for the actual concurrency levels observed in production.
async def reconcile_transaction(txn_id: str) -> ReconciliationResult:
# Previously sequential — 3x the latency
positions, trades, settlements = await asyncio.gather(
fetch_positions(txn_id),
fetch_trades(txn_id),
fetch_settlements(txn_id),
)
return compute_reconciliation(positions, trades, settlements)
The Kubernetes infrastructure was refactored for horizontal scaling. The reconciliation workers were separated from the API layer, allowing them to scale independently based on queue depth. Pod disruption budgets and proper readiness/liveness probes were added — the kind of operational hardening that prevents the cluster from taking down its own workloads.
PostgreSQL query optimization was a significant part of the work. Financial queries tend to be complex: multiple joins, aggregations, date-range filters across large tables. The query analysis (using EXPLAIN ANALYZE on every slow query identified by the observability layer) revealed several cases of sequential scans on large tables where an index would have been straightforward to add. Index design on partitioned tables requires care in PostgreSQL.
The Outcome
Production incidents dropped 40% in the quarter following the observability and async refactor work. The mean time to resolution for incidents that did occur dropped further — because the observability layer made root cause analysis a matter of minutes rather than hours of log archaeology.
The team's deployment confidence improved measurably. The feature delivery rate in the following quarter was higher than in any previous quarter.
Lessons
Observability before optimization. Every engineering team thinks they know where the bottlenecks are. The teams that actually know have the data. The observability work at JFIntech revealed that the assumed bottleneck (the third-party reconciliation API) was not the actual bottleneck. The actual bottlenecks were internal and fixable within a week.
Async is not always the answer. The reconciliation operations were the right candidates for async refactoring because they were genuinely independent. Other parts of the codebase had sequential operations that were sequential for reasons (transaction ordering guarantees, for example). Understanding which sequential operations can safely be parallelized requires domain knowledge, not just profiling data.
In financial systems, test your failure modes explicitly. The 40% incident reduction came partly from the fixes and partly from adding explicit tests for the failure scenarios that had been causing incidents. Tests that prove the system degrades gracefully under load are as valuable as tests that prove it works correctly under normal conditions.