14 siloed source systems. Manual regulatory reporting consuming 12 analysts and three weeks per cycle. We consolidated everything into a single Snowflake platform and cut reporting time to under three days.
A top-30 financial services firm was operating trading systems, risk models, CRM, core banking, and nine additional source systems with no integration layer and no single authoritative data source. Regulatory reporting (CCAR, BCBS 239) was entirely manual: 12 analysts running spreadsheet-based reconciliations for three weeks every reporting cycle.
Data lineage was undocumented, which created audit exposure. When examiners asked how a number was derived, the answer required manually tracing through multiple systems. The firm had been flagged in an internal audit for lineage gaps and needed to demonstrate a remediation path to the board risk committee.
The reporting problem was operationally painful: 12 analysts locked in a three-week sprint every quarter, running reconciliations that should take hours. But the audit exposure was the harder issue. BCBS 239 requires documented data lineage for all risk-aggregation inputs. Without it, the firm was carrying regulatory risk that examiners had already noticed.
The 14 source systems also had inconsistent data definitions for the same concepts. "Trade date" meant different things in three systems. "Credit exposure" was calculated three different ways. Building a unified platform required resolving these semantic conflicts before any technology decisions, or the consolidation would just move the reconciliation problem from spreadsheets to SQL.
Before writing any Snowflake SQL, we spent three weeks on data modeling workshops with source system owners to resolve semantic conflicts. Every concept that appeared in more than one system got a canonical definition, a data owner, and a contract. The platform was built on top of agreed facts, not assumptions.
Every Ingress engagement runs the same four-stage delivery model. Here is how Aizen applied to this financial data platform build — from the semantic discovery sprint to the SOC 2 audit-ready operating state.
Three-week discovery to resolve conflicting data definitions across systems. Output: canonical data dictionary, ownership matrix, and a written brief on the real problem — audit exposure, not just slow reporting.
Architecture decision record documented before a single Snowflake object was created. Bronze / Silver / Gold layer responsibilities defined. Data contracts written and signed by source system owners before build started.
Phased production delivery: Bronze layer first, then Silver conformance, then Gold reporting layer. CCAR and BCBS 239 runs automated by month 5. Every dbt model shipped with documented column-level lineage.
Governance controls operational. SOC 2 Type II program running. Client data team trained on dbt and Snowflake. Otonmi NL-to-SQL layer deployed. Ingress rolled off with full documentation and zero outstanding runbook gaps.
The unified Snowflake platform went to production in six months. The quarterly regulatory reporting cycle, previously a three-week manual sprint consuming 12 analysts, now runs in under three days as an automated process with human review. BCBS 239 data lineage is fully documented at the column level, resolving the examiner exposure flagged in the prior audit.
Annual analyst time savings exceed 2,400 hours. SOC 2 Type II report was issued at the end of year one. Executive dashboards in Power BI gave the CFO and Chief Risk Officer real-time views of key risk and performance metrics that previously required a data request with a 72-hour turnaround. The Otonmi NL-to-SQL analytics layer let non-technical business users query the platform directly, removing the backlog from the data team.
Quarterly CCAR and BCBS 239 submissions now run as automated processes. The 12-analyst sprint is a historical reference, not an operating model.
Every risk aggregation input traceable from source table to regulatory output. Examiner exposure from prior audit fully remediated.
Data access controls, change management, and availability SLAs documented and tested. Type II report issued with no exceptions noted.
Time previously spent on manual reporting reconciliation redirected to analysis, model validation, and strategic work.
Medallion architecture (Bronze, Silver, Gold). Snowflake Data Sharing for cross-team access. Dynamic data masking and row-level security for SOC 2 and PCI-adjacent data.
dbt Core for all transformations with automated documentation and column-level lineage tracking. dbt Data Contracts for inter-team source agreements. Airflow for orchestration.
Power BI semantic layer for executive and risk dashboards. Otonmi natural-language-to-SQL analytics for self-serve business user queries against the Gold layer without data team involvement.