Snowflake for RIAs. The Data Layer Behind the Multi-Custodial Firm.

Most RIAs today run somewhere between six and twelve software systems. Each one holds a slice of the client picture. Schwab holds positions. Orion holds performance history. Salesforce holds relationship notes. MoneyGuidePro holds planning scenarios. None of them talk to each other at the data layer. The result is a firm that knows a lot but can see very little of it in one place at one time.

Snowflake changes that architecture. It is not another reporting tool. It is the data layer underneath your reporting tools — a cloud warehouse where every system's data lands, normalizes, and joins. For an overview of how this fits the broader financial services landscape, see our Snowflake for Financial Services pillar.

Why RIAs Are Choosing Snowflake

The shift toward Snowflake among growth-oriented RIAs is not driven by technology enthusiasm. It is driven by a specific operational pain that accumulates as firms scale: the multi-custodial reconciliation problem.

The Multi-Custodial Reconciliation Problem

An RIA with $2 billion in AUM typically has clients at three to five custodians. Each custodian delivers overnight position files in its own format, on its own schedule, with its own quirks. A Schwab position file does not look like a Fidelity position file. Pershing's transaction codes do not match Altruist's. Reconciling these into a single book view requires either expensive custom middleware or manual work by back-office staff who spend Monday mornings correcting Friday's numbers.

Snowflake does not solve the custodian data problem alone — but it gives firms the platform to solve it once, correctly, and permanently. Pre-built connectors normalize each custodian's data into a consistent schema. Once in Snowflake, a Schwab account and a Fidelity account are just accounts in the same table. Cross-custodian queries run in seconds.

The Reporting Tool Ceiling

Portfolio reporting tools like Orion, Black Diamond, and Tamarac are excellent at what they were designed to do: produce client-facing performance reports and support rebalancing workflows. They were not designed to be analytical data warehouses. Their data models are proprietary. Their query interfaces are limited. Joining portfolio data with CRM data or billing data requires exporting to spreadsheets and assembling manually.

Snowflake sits behind these tools, not in place of them. It receives data from Orion, from Schwab, from Salesforce — and makes all of it queryable together. The reporting tool keeps doing what it does well. Snowflake handles what it cannot.

The AI Readiness Problem

RIAs increasingly want to use AI for client segmentation, churn prediction, and advisor productivity analytics. AI models require clean, structured, queryable data — exactly what Snowflake provides. A firm whose data lives in custodian portals and portfolio system exports cannot effectively leverage AI tools. A firm whose data lives in Snowflake can. See also: AI-Ready Data for Wealth Management.

What Lives in a Snowflake-Powered RIA Stack

A Snowflake-powered RIA data stack is not a single system — it is a data layer that receives from, enriches, and feeds back into every major system the firm uses. Understanding what data lives in Snowflake clarifies why it is foundational rather than redundant.

Custodian Feeds

Daily position, transaction, and account files from Schwab, Fidelity, Pershing, Altruist, and any other custodian the firm uses. Pre-built connectors normalize each custodian's proprietary format into a consistent schema. Accounts, positions, and transactions at different custodians share common column names and data types — enabling cross-custodian analysis with standard SQL.

Portfolio Management System

Performance history, account groups, benchmarks, and model allocations from Orion, Black Diamond, Tamarac, or Addepar. Snowflake receives this data and enables analysis that the portfolio system's own reporting cannot support — such as comparing performance across custodians, linking performance to client segmentation data, or joining performance records with billing histories.

CRM Data

Client relationship data from Salesforce, Redtail, Wealthbox, or Practifi — including household structure, relationship type, referral source, life events, and service tier. Joined with portfolio data in Snowflake, CRM data enables client segmentation analytics that reveal which client profiles are most profitable, which have highest attrition risk, and where advisory capacity is best deployed.

Billing and Fee Data

Advisory fee schedules, billable AUM calculations, and fee revenue by client, advisor, and service tier. Billing data in Snowflake enables revenue analytics that portfolio systems and CRMs cannot produce in isolation — monthly recurring revenue by segment, effective fee rate by AUM tier, revenue impact of client churn or growth.

Financial Planning Outputs

Planning scenario data from MoneyGuidePro, eMoney, or RightCapital — including projected retirement income, Monte Carlo results, and goal-funding status. When planning outputs land in Snowflake alongside actual portfolio data, advisors can track how client outcomes are tracking against plan and surface accounts where a planning conversation is overdue.

How Snowflake Solves the Multi-Custodial Reconciliation Problem

Reconciliation is the unglamorous core of RIA operations. Every business day, positions held at multiple custodians must be verified against portfolio system records. Discrepancies must surface. Exceptions must be investigated and resolved. For firms running this manually, reconciliation consumes hours of back-office time every morning — time that does not scale linearly with AUM growth.

The Normalization Layer

Snowflake's role in reconciliation begins with normalization. Pre-built connectors pull each custodian's overnight files and transform them into a common schema: a standardized account table, a standardized position table, a standardized transaction table. Security identifiers are resolved to a common reference (typically CUSIP or ISIN). Custodian-specific account codes are mapped to the firm's internal account structure. This normalization happens before any analyst touches the data.

Once data is normalized, cross-custodian reconciliation becomes a SQL query rather than a manual spreadsheet exercise. Position counts from Schwab and Fidelity on the same security are directly comparable because they share the same schema. Discrepancies between custodian records and portfolio system records surface as exceptions in a structured report rather than requiring manual comparison.

Automated Exception Reporting

With all custodian data in Snowflake and the portfolio system feeding records into the same warehouse, automated exception detection becomes straightforward. A scheduled query runs each morning, comparing custodian position balances against portfolio system records, flagging any account where the discrepancy exceeds a defined threshold. Back-office staff see a prioritized exception list rather than raw position files to compare manually.

The same approach applies to cash reconciliation, transaction matching, and corporate action processing. Each reconciliation type becomes a repeatable query rather than a manual process — consistent, auditable, and scalable as the firm grows without adding back-office headcount.

Snowflake for RIAs vs. Building Your Own Stack

Some RIAs — typically those with engineering resources and a specific vision for their data infrastructure — choose to build Snowflake integrations in-house. Others work with a platform like Milemarker that brings pre-built connectors, a purpose-built data model, and managed pipelines. The right choice depends on the firm's engineering capacity, timeline, and long-term infrastructure ambitions.

Both paths lead to the same outcome: a Snowflake data warehouse the RIA fully owns and controls. The difference is time, cost, and ongoing maintenance burden. For firms without a dedicated data engineering team, the build-in-house path routinely takes 18 months and significant budget before any analytics are possible. Milemarker's approach compresses that to weeks.

Where Milemarker Fits

Milemarker is a Snowflake-native platform built specifically for wealth management firms. For RIAs, that means pre-built connectors to every major custodian and portfolio system, a purpose-built RIA data model, and managed pipelines that keep data flowing without requiring internal engineering resources to maintain them.

Augments, Never Replaces

Milemarker does not replace your existing Snowflake setup if you have one. It augments it. If you have already built pipelines from Schwab or Orion, Milemarker can add the connectors you are missing, extend your data model with Milemarker's purpose-built schema, or take over pipeline maintenance so your engineering team can focus on higher-value work.

If you are starting from zero, Milemarker builds the entire Snowflake infrastructure — account provisioning, connectors, data model, and BI layer — deploying into a Snowflake account that you own. Your data never lives in a Milemarker-controlled black box. The Infrastructure for Wealth is the phrase we use to describe this: foundational, firm-owned, purpose-built.

Milemarker Navigator

For RIAs that want to move from raw data to operational analytics quickly, Milemarker Navigator is a pre-built analytics layer that runs on your Snowflake warehouse. AUM dashboards, client segmentation reports, billing analytics, and advisor productivity metrics — all available out of the box and fully customizable because they read from your warehouse directly.

For RIAs exploring a broader data platform strategy beyond Snowflake-specific infrastructure, see our guide to data warehouse architecture for RIAs and our overview of wealth management data platforms more broadly.

Frequently Asked Questions