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A leading US digital banking platform operated multiple financial institutions, each operating in separate, disconnected cloud environments with no shared analytics. We designed and built a hybrid-cloud data platform that unified their entire portfolio, delivering near-real-time dashboards, strict tenant isolation, and cross-FI intelligence where there was none before.
Digital Banking Platform (Multi-FI SaaS) — Confidential
7 Months
Fintech / Digital Banking
United States
The client operated multiple Financial Institutions (FIs), each running in fully isolated Azure environments with independent SQL databases and no shared analytics capability. They needed to consolidate cross-FI data without compromising tenant-level security, build embedded BI inside their existing admin portal, and do it all without disrupting production systems already in use.
No centralized analytics warehouse or unified data model; every FI operated in its own disconnected environment
Inconsistent KPI definitions across institutions made cross-FI comparison impossible
Analytics had to be embedded into an existing Azure C# admin portal, with no greenfield build
Near real-time sync required: <15 minutes latency across all FI data sources
High operational overhead to maintain per-tenant analytics with no scalable onboarding path
Zero visibility into cross-FI product adoption, engagement benchmarking, or FI performance
Strict tenant isolation required; data from one FI could never be accessible to another
Scalable onboarding framework needed to bring new FI tenants live with minimal engineering effort
The client had strong operational foundation: multiple live financial institutions, real user bases, and a clear product vision. What was missing was the data infrastructure to connect it all. Every architecture decision started from two constraints: don't touch the existing Azure production environments, and don't compromise FI-level data isolation under any circumstances.
No centralized data lake: each FI's analytics were isolated, unconnected, and manually maintained
No cross-FI benchmarking capability; performance comparisons required manual exports and spreadsheets
No embedded BI: FI admins had no analytics inside their existing portal workflow
No executive visibility: leadership had no aggregated view of product adoption across the portfolio
No scalable onboarding; adding a new FI required significant manual engineering effort each time
Competing on product adoption metrics with no reliable data to measure or act on them
Analytics at scale across multiple isolated financial institutions isn't a reporting problem; it's an architecture problem. The platform needed a unified data layer that preserved every compliance and isolation requirement while unlocking portfolio-level intelligence that had never existed before.
Consolidating Without Compromising Isolation
Each FI operated in a fully isolated Azure environment for legitimate compliance reasons. The challenge was extracting and centralizing only the non-PII business data needed for analytics, without creating any path for cross-tenant data leakage. Row-Level Security in the warehouse layer had to be airtight.
Standardizing KPIs Across Institutions
With each FI defining metrics differently, a dashboard showing "active users" meant something different depending on which institution you were looking at. Before any analytics could be meaningful, a unified KPI framework had to be defined, documented, and enforced through the ETL transformation layer.
Embedding BI Without Rebuilding the Portal
The client's Azure C# admin portal was already in use by FI administrators. Adding analytics had to feel native, not like a third-party tool bolted on. The QuickSight embedding via iframe needed to load fast, respect existing session contexts, and surface only the data the logged-in FI was permitted to see.
Building for the Next FI, Not Just This One
The client's business model depended on adding new financial institutions over time. Every pipeline, schema, and dashboard design had to be modular enough that onboarding a new FI was a configuration exercise, not a new engineering project. Under four hours from intake to live analytics was the bar.
We started with the constraint that mattered most: zero disruption to live Azure production environments. Every architectural choice, from hybrid cloud to serverless ETL and event-driven triggers, was made to extract and transform data without touching what was already running. The analytics layer had to be additive, not invasive.
We implemented Change Data Capture pipelines in Azure Data Factory to extract incremental updates from each FI's Azure SQL database. Initial full loads were followed by delta-based sync, ensuring only relevant non-PII business data crossed into the analytics environment. Azure Key Vault handled secure cross-cloud credential management throughout.
AWS Lambda served as the core ETL processing engine. Triggered by S3 file ingestion events and EventBridge scheduled jobs, Lambda functions handled data validation, schema standardization across all FI datasets, KPI computation, and enrichment. The stateless architecture enabled horizontal scaling and fault tolerance without infrastructure management overhead.
S3 served as the centralized data lake with distinct zones for raw ingested data and processed analytics-ready records. This separation allowed us to replay transformations when KPI logic evolved, without losing source fidelity. The modular zone structure also made it straightforward to onboard new FI tenants into the existing pipeline.
Amazon RDS (PostgreSQL) served as the centralized analytics warehouse with a schema optimized for KPI aggregation and pre-aggregated structures for performance-critical dashboards. Row-Level Security was implemented at the database level, not just the application layer, to enforce strict FI-level data isolation and enable both tenant-scoped and portfolio-level queries from a single warehouse.
Amazon QuickSight was embedded into the existing Azure C\# admin portal via iframe, providing FI administrators with seamless analytics access without leaving their current workflow. A unified KPI framework covering user engagement, feature adoption, loan behavior, savings activity, and financial planning was implemented consistently across all institutions, making cross-FI benchmarking possible for the first time.
Eleven components built to function as a single cohesive platform rather than a collection of standalone tools. Each one addressed a specific constraint: getting data flowing reliably, keeping tenants isolated, surfacing insights to the right audience, and making the whole system operable without ongoing engineering intervention.
Change Data Capture pipelines extracting incremental updates from each FI's Azure SQL database. Initial full load followed by continuous delta sync, with non-PII filtering built into the extraction layer.
Event-driven Lambda functions handling validation, schema standardization, KPI computation, and data enrichment. Stateless and horizontally scalable, with no infrastructure to manage as data volumes grow.
Centralized data lake with distinct zones for raw ingested data and analytics-ready processed records. Enables transformation replay and clean onboarding of new FI tenants without pipeline rearchitecture.
Amazon RDS PostgreSQL with optimized schema for KPI aggregation, pre-aggregated structures for dashboard performance, and Row-Level Security enforcing strict FI-level data isolation at the database layer.
FI-level isolated dashboards embedded directly into the existing Azure admin portal. Each FI sees only its own data. Internal stakeholders access cross-FI portfolio views through a separate permissions layer.
Unified metric definitions covering active users, login frequency, loan behavior, task completion, savings goal adoption, and video engagement, consistent across all institutions and queryable at tenant or portfolio level.
Executive dashboards enabling engagement benchmarking, product adoption comparison, performance scoring, and heatmaps across features; the first time this level of visibility existed across the entire FI portfolio.
Modular pipeline architecture allowing new financial institution tenants to be onboarded through configuration rather than custom engineering, reducing time-to-live analytics to under four hours per new FI.
End-to-end observability across the hybrid pipeline, from Azure ingestion through Lambda processing to QuickSight delivery. Alerts configured for ETL failures, latency breaches, and dashboard availability thresholds.
The platform went live across all financial institutions with 100% FI adoption on rollout. For the first time, the client had a single view of their entire portfolio, with near-real-time data, sub-two-second dashboards, and an onboarding process fast enough to keep pace with their growth. Here's what the platform delivered:
<15 min
End-to-end data freshness from FI source systems to live dashboards
99.5%
ETL pipeline success rate across all financial institution data sources
<2 sec
Dashboard load time, meeting the responsiveness standard of consumer-grade products
60%
Reduction in analytics operational overhead, from manual per-tenant maintenance to automated pipelines
100%
FI adoption across the entire rollout; every institution live on the platform at launch
4 hrs
Time to onboard a new financial institution tenant from intake to live analytics
99.9%
Dashboard availability, meeting the uptime standard expected for a business-critical internal tool
5+
Executive-level cross-FI portfolio insights delivered monthly, a capability that didn't exist before
"The Solvios team brought architecture clarity to a genuinely complex problem. What we needed wasn't just a reporting tool. We needed an entirely new data layer that could serve multiple financial institutions simultaneously without any of them ever seeing each other's data. They delivered that, and they built it to grow with us."
— Urvil, Director of Engineering, Multi-FI Digital Banking PlatformThis was an agile, iterative engagement. Rather than handing off requirements and waiting, the client's product and business teams were involved at every stage, with working software reviewed and validated in short cycles throughout the seven months.
Joint working sessions with product, engineering, and business stakeholders to audit existing FI environments, define KPIs, and agree on the hybrid-cloud architecture together. Decisions were made collaboratively rather than handed down, so the team had shared ownership of the approach before a line of code was written.
Azure Data Factory CDC pipelines and the AWS Lambda ETL framework were built and reviewed incrementally, with each FI's ingestion validated against real data before moving to the next. Client feedback on data quality and schema behaviour was incorporated in sprint cycles rather than saved for a final review.
The PostgreSQL warehouse schema and KPI computation logic were developed alongside the client's business and product teams, not handed to them as a finished spec. KPI definitions were reviewed, challenged, and refined in working sessions so the final framework reflected how the business actually measured performance, not how an engineer assumed it did.
QuickSight dashboards went through multiple rounds of review with the FI administrators who would actually use them, not just internal stakeholders. Layout, filtering behaviour, and data presentation were adjusted based on real user feedback before embedding into the Azure portal, so the experience was validated in context rather than signed off in isolation.
FI tenants were onboarded in controlled phases rather than a single go-live, allowing each rollout to inform the next. CloudWatch monitoring data and post-launch feedback from administrators fed directly back into the team's sprint cycle, with improvements shipped continuously rather than batched into a post-launch release.
Isolation is enforced at multiple layers, not just the application level. During ETL, each FI's data is tagged and processed through tenant-scoped pipelines. In the PostgreSQL warehouse, Row-Level Security policies restrict query results based on the authenticated session's FI context. QuickSight row-level security adds a final application-level check. No single failure point can expose cross-tenant data because the architecture doesn't rely on one.
For a project of this scope: multi-tenant ingestion, serverless ETL, embedded BI, and KPI standardization: seven months is representative. Discovery and architecture design typically take three to four weeks; pipeline development and warehouse build another eight to ten; dashboard development and portal embedding four to six; and rollout with monitoring two to three. Timelines compress when KPI definitions are pre-agreed and FI environments are well-documented.
Yes, that was exactly the constraint here. The Azure C\# admin portal was already in production use. QuickSight was embedded via iframe with session-level authentication passed through, so FI administrators see analytics inside their existing workflow without any context switch. The key is that embedding is configured at the QuickSight and application layer simultaneously, not treated as a bolt-on afterthought.
The onboarding framework is designed to be configuration-driven, not code-driven. Each new FI gets a provisioning process that registers the data source, configures CDC extraction parameters, maps to the standard schema, and initializes RLS policies, all through a defined runbook, not custom engineering. Under four hours to live analytics for a new FI was the target, and the architecture was built around hitting it consistently.
Scope varies significantly depending on the number of tenants, complexity of existing environments, and depth of KPI framework required. We scope based on a detailed discovery phase and provide a fixed-price proposal before any development begins. Reach out to start the conversation and we'll tell you what's realistic for your specific situation.
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We work with fintech and SaaS teams who need to unify data across multiple tenants, embed analytics without rebuilding their portal, or build the cross-account visibility their product has never had. If you're consolidating fragmented cloud environments or architecting for a portfolio that keeps growing, we'd like to hear about it.
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