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

The Challenge

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

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

savings

Snowflake optimization

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

Data modeling with dbt

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

Integration reliability

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

Results

savings

30% Snowflake cost reduction

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

Clear, documented data models

improved collaboration and onboarding for engineering and analytics teams.
bolt

Faster analytics

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

Stronger integrations

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

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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