/ Selected work

Real systems. Real names.

Active AIM Metrics client work and the enterprise platforms our founders built and led in-house — labeled honestly, so you know exactly which is which.

Energy / Fortune 500

NRG Energy

AIM Metrics client

Marketing analytics engineering for NRG Energy

An active AIM Metrics engagement with one of America's largest energy companies: custom marketing analytics, reporting, and dashboards — built on aggregated, privacy-safe data and delivered fully remote.

Data Engineering

F500
enterprise client, direct engagement: 100%
remote, project-based delivery: 0
PII touched — aggregated data only

Smart Home / IoT

Vivint Smart Home

In-house leadership

Modernizing Vivint's enterprise data platform

Our founder directed Vivint's data warehouse and BI engineering org through a full platform modernization — migrating from SQL Server to Snowflake and redesigning the architecture for performance, scale, and cost.

Legacy ModernizationData Engineering

200+
internal data consumers served: SQL→❄
full SQL Server to Snowflake migration: 3+ yrs
of platform leadership

Consumer Internet / Genealogy

Ancestry

In-house leadership

Re-architecting the warehouse behind Ancestry's growth years

Eight years inside Ancestry's data organization — migrating the enterprise warehouse to MPP, breaking a monolithic ETL architecture into modular micro-pipelines, and shipping the LTV model executives ran the business on.

Legacy ModernizationData Engineering

8 yrs
building Ancestry's data backbone: ↓ batch
times via modular micro-pipelines: LTV
company-wide forecast model shipped

Professional Sports

New England Patriots

In-house leadership

Standing up the BI function for the New England Patriots

Our founder built and led the business intelligence function for the Patriots — implementing a modern data stack (Snowflake, Snaplogic, Tableau, AWS) and reusable integration frameworks that made new data sources fast to onboard.

Full-Stack ProductData Engineering

1st
modern data stack for the franchise: ↓↓
time to onboard new data sources: E2E
stack, vendors, budget & team owned

Software

B2B SaaS company

Name withheld

Cutting LLM spend 68% while improving answer quality

An AI feature shipped fast and successful — then the model bill became a board-level line item. We rebuilt the inference layer with routing, caching, and an evaluation harness that proved quality didn't regress.

AI Systems

68%
reduction in LLM spend: 2.1x
faster median response: +9 pts
eval accuracy improvement

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