Executive Summary
A mid-market SEC-registered investment adviser modernized its data platform to deliver near real-time portfolio visibility and governed reporting. Using Fivetran for automated ingestion, dbt for modular transformations and semantic modeling, Snowflake for elastic warehousing, and Power BI for governed self-serve analytics, the firm consolidated APX, custodian, CRM, and finance data into trusted marts. The implementation cut quarterly reporting cycle time by half, reduced defects by over 90%, and improved compliance readiness, while lowering pipeline maintenance and improving adoption.
Business Challenges
- Fragmented data across Advent APX, custodian files, Salesforce, and finance systems caused reconciliation breaks and slow cycles.
- Manual, CSV-driven reporting required multiple analysts for weeks each quarter; compliance extracts took 5–7 business days.
- Limited transparency into data lineage, inconsistent KPIs, and low analyst trust in numbers.
- Need for near real-time positions/trades and governed access with book-of-business isolation.
Objectives
- Centralize and standardize data with minimal operational overhead.
- Deliver near real-time positions, trades, and cash with defined SLAs.
- Establish conformed dimensions, governed marts, and consistent KPIs for investment, operations, and compliance teams.
- Enforce robust security (RLS, masking), SSO, and full auditability.
Solution Overview
We implemented a Snowflake-centered analytics platform with managed ingestion and modular modeling for reliable pipelines and governed, performant BI.
Data ingestion with Fivetran
- Prebuilt connectors load Salesforce and QuickBooks into Snowflake RAW schemas on automated, incremental schedules (typically every 15 minutes).
- Custodian and APX exports land via staged file ingestion (S3/SFTP) with Snowpipe Auto-Ingest for hourly updates and backfills.
- Connector health, sync logs, and alerts provide operational visibility; schema drift handled automatically.
Transformations and modeling with dbt
- A layered dbt project (stg_ → dim_/fct_ → mart_) conforms data, applies quality checks, and assembles a single source of truth for portfolio, performance, revenue, and compliance.
- Patterns include incremental models for trades/positions, SCD Type 2 for client-rep mappings, and conformed dimensions (dim_client, dim_account, dim_security).
- Data quality via dbt tests (unique, not_null, relationships, accepted_values) and schema contracts; exposures document lineage to Power BI assets.
Analytics and access with Power BI
- Power BI connects to Snowflake marts (DirectQuery for near real-time, Import where appropriate) to deliver Portfolio Health, Trade Exceptions, Compliance Monitoring, and Revenue Attribution.
- Certified datasets and governed app workspaces ensure consistent metrics and controlled distribution; row-level security is enforced in Snowflake.
- Deployment pipelines (Dev/Test/Prod) and endorsements streamline releases and adoption.
Architecture
Implementation Details
#1: Data Integration and Standardization
- Tools : Fivetran, Snowflake, dbt
- What we did :
- Configured Fivetran connectors for Salesforce and QuickBooks; raw data landed in RAW schema with source-specific naming.
- Automated custodian and APX file ingestion via S3/SFTP and Snowpipe Auto-Ingest; implemented historical backfills and hourly refresh.
- Built dbt staging models (stg_) to normalize instrument identifiers (CUSIP/ISIN), standardize timestamps/time zones, enforce types, deduplicate, and reconcile account-to-client mappings.
- Applied dbt tests and schema contracts upstream of marts to ensure reliability.
#2: Core Modeling and Subject Marts
- Tools : dbt, Snowflake
- What we did :
- Constructed conformed dimensions : dim_client (SCD2), dim_account (SCD2), dim_security; reference tables for custodians and advisors.
- Built fact layers for fct_trades, fct_positions_daily, fct_cash_movements, fct_performance_snapshots, and fct_revenue.
- Implemented business rules : trade_date ≤ settle_date, negative share checks, reasonability thresholds for accruals/fees, custodian vs APX reconciliation tolerances by asset class.
- Created Gold marts for Investment (positions/performance), Operations (exceptions/reconciliations), Compliance (regulatory extracts readiness), and Finance (revenue/fees).
#3: Security, Governance, and Cost Management
- Tools : Snowflake, Azure AD, Power BI
- What we did :
- Enforced Azure AD SSO, Snowflake masking policies for PII, and row access policies for book-of-business isolation.
- Established separate warehouses for ELT and BI; autosuspend (60s) and resource monitors to control spend; query tagging for chargeback and optimization.
- Published dbt docs for end-to-end lineage; Power BI lineage and endorsements for certified datasets.
#4: Orchestration and Operations
- Tools : dbt Cloud (or scheduled runs), Snowflake Tasks/Streams, Fivetran
- What we did :
- Scheduled incremental dbt runs aligned with Fivetran syncs and Snowpipe events.
- Used Snowflake Tasks and Streams to detect upstream changes and trigger downstream refreshes and exception checks.
- Monitored freshness, test pass rates, and sync success (>99.7%) with alerting to Slack/Email.
#5: Consumption and Decision Support
- Tools : Power BI
- What we did :
- Delivered domain apps :
- Investment : Portfolio Health (T+0 positions, drift >2% alerts, performance drilldowns)
- Operations : Trade Exceptions and Reconciliations (breaks by custodian/account, time-to-resolution)
- Compliance : Filing readiness, data completeness, pipeline health
- Finance : Revenue Attribution by product, client segment, and advisor
- Implemented certified datasets with clear owners, refresh SLAs, and RLS; enabled ad-hoc analysis on governed models.
Data Models and Tables (examples)
| Staging | stg_apx_tradesstg_apx_positionsstg_custodian_positionsstg_salesforce_accountsstg_quickbooks_invoices |
|---|---|
| Core | dim_client (SCD2)dim_account (SCD2)dim_securityfct_tradesfct_positions_dailyfct_cash_ movementsfct_revenue |
| Marts | mart_investment_positionsmart_performance_overviewmart_ops_exceptionsmart_compliance_readinessmart_revenue_attribution |
| Monitoring | met_data_freshnessmet_dbt_test_resultsmet_reconciliation_status |
Outcomes
Reporting and efficiency
- Quarterly reporting effort reduced from ~240 hours to ~112 hours across three cycles.
- Pipeline maintenance time reduced by ~62% after migrating to Fivetran + dbt.
Data quality and reliability
- QA defects per reporting cycle decreased from 13.2 to 1.0; dbt test pass rate improved to 98.8%.
- Sync success rate >99.7%; reconciliations surfaced and resolved earlier via exception dashboards.
Latency and performance
- Salesforce and QuickBooks data refresh every 15 minutes; custodian/APX files hourly; near real-time positions/trades where supported.
- Snowflake p95 query times under ~2.3 seconds on certified reports even as data volume scaled ~4.6x.
Adoption and satisfaction
- 55 monthly active Power BI users; 21 certified reports; 78% DAU within investment and operations.
- NPS improved from 46 to 51 post-launch; faster turnaround on data requests (SLA attainment up from 65% to 88%).
Compliance readiness
- All regulatory deadlines met for three consecutive quarters; complete audit trails and lineage simplified reviews.
Tech Stack
Snowflake
- Elastic compute isolation via virtual warehouses, separation of storage and compute, Time Travel, secure data sharing, and robust governance features.
Fivetran
- Managed connectors and scheduling reduce pipeline maintenance; predictable costs with MAR-based pricing.
dbt
- Transformation-as-code with built-in testing, documentation, and CI/CD for reliable analytics engineering.
Power BI
- Widely adopted enterprise BI with strong governance and security, ideal for executive and operational reporting.
Implementation Timeline
Weeks 0–2
- Discovery, source profiling, data contracts, KPI definitions
Weeks 3–6
- Fivetran connectors, Snowpipe for files, RAW landing (Bronze)
Weeks 7–10
- dbt STG/CORE (Silver), initial marts (Gold) for positions/trades/performance
Weeks 11–14
- Certified Power BI datasets, RLS, workspace governance, deployment pipelines
Weeks 15–18
- UAT, performance tuning, training, rollout; iterative dashboard expansion thereafter
Representative KPI Examples
- Reporting cycle time : 240h → 112h
- Defects per cycle : 13.2 → 1.0
- Data request SLA attainment : 65% → 88%
- Sync success : >99.7%
- p95 query time on certified reports : <2.3s
Testimonial
Our quarterly statements now close in days, not weeks—and the investment team trusts the numbers. We gained real-time visibility without adding maintenance overhead.
- COO
Team Composition
- Project Manager: 1
- Data Architects: 2
- ETL Developers: 2
- Database Administrator: 1
- BI Developer: 1
- Quality Assurance Specialist: 1
- Support Staff: 1
