Venture Capital

Data Platform Optimization for a $15B Global VC Firm

A global venture capital firm managing $15 billion, known for high-conviction, concentrated investments in category-defining technology companies.

Duration
~1 year
Team
1 embedded data engineer
Services
Data Platform Optimization
Tech Stack
Snowflake
dbt
Dagster
Hex
CRM integrations
Sigma
Clay
The Challenge

What stood in the way.

The firm's data platform had grown complex — multiple external and internal data sources flowing into a Snowflake warehouse, with pipelines spanning diverse tooling. Snowflake costs were rising, data models lacked documentation, and integrations across the toolchain needed stabilization. The volume and diversity of data made managing, transforming, and monitoring pipelines increasingly difficult.

The Solution

How we built it.

We embedded a single senior data engineer into the firm's internal team, focusing on warehouse optimization and pipeline reliability.

30%
cost reduction
$10K/mo
saved

Snowflake optimization

improved queries, incremental models, and warehouse finetuning to reduce costs and improve performance.

Data modeling with dbt

adoption of medallion architecture principles to create clear, documented, modular data models. Improved collaboration between data engineers and analytics teams.

Integration reliability

stabilized the toolchain connecting notebooks (Hex), CRM (Affinity), dashboards (Sigma), orchestration (Dagster), and enrichment tools (Clay) to the central warehouse.
Results

What we delivered.

30% Snowflake cost reduction

approximately $10,000 per month saved within the first months of the engagement.

Clear, documented data models

improved collaboration and onboarding for engineering and analytics teams.

Faster analytics

optimized pipelines made dashboards and notebooks run more efficiently, reducing time-to-insight.

Stronger integrations

cleaner warehouse structure enabled tighter connections with CRM and enrichment tools, supporting automation.

Clean handoff

engagement completed in approximately one year by design. The firm's in-house team now operates independently on the foundation we established.
Why This Matters

Not every problem requires a large team.

One senior engineer, embedded in your workflow, delivered a 30% Snowflake cost reduction — roughly $10,000 per month in savings — while documenting data models, stabilizing integrations, and leaving a clean foundation the in-house team now operates independently. Sometimes the highest-impact move is precision, not scale.

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THE NEXT LEVEL

Let's talk about what
you're building

An AI-native partner that's already done to itself what it now does for its clients.

ENGINEERING
15 years depth
CLIENT AUM
$1.2 trillion+
NPS SCORE
80+
PARTNERSHIP
AI-native partner