Data Engineering built for Wealth Management companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Wealth Management companies, this means navigating SEC regulations (US), MiFID II (EU) while shipping fast.
Who needs Data Engineering for Wealth Management?
- Fragmented portfolio data across custodians
- Client reporting requiring manual aggregation
- Robo-advisory algorithm transparency expectations
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Wealth platforms must turn complex portfolio data into something both advisors and clients trust.
Wealth Management founders and CTOs in trust GarudLabs with Data Engineering because we communicate like an in-house team.
Talk to our Wealth Management Data Engineering specialists this week
Book a free 30-minute discovery callWhy Wealth Management companies in struggle with Data Engineering
- Fragmented portfolio data across custodians
- Client reporting requiring manual aggregation
- Robo-advisory algorithm transparency expectations
- Regulatory compliance across investment products
- Security expectations for high-net-worth client data
What makes our Data Engineering different for Wealth Management businesses in
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- SEC regulations (US)
- MiFID II (EU)
- GDPR
- FINRA compliance
Proven Results
"We helped a wealth management platform cut quarterly reporting time by 55% with automated portfolio aggregation."
Read the full case studyStop guessing on Data Engineering — let's build a real plan together
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple wealth management projects addressing challenges like fragmented portfolio data across custodians. We helped a wealth management platform cut quarterly reporting time by 55% with automated portfolio aggregation.
For wealth management clients, we pay close attention to SEC regulations (US), MiFID II (EU), building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for wealth management clients include fragmented portfolio data across custodians and client reporting requiring manual aggregation. We tailor our discovery process to surface the specific version of these problems your business faces.