Selected systems and outcomes

Production proof, constraints included.

What changed, what made it difficult, and what shipped. Founder work is labeled separately from professional experience.

GreenRoom TechnologiesFounding engineer and cofounderV2 beta in progress

Rebuilding a paid networking product around structured professional proof.

GreenRoom V1 validated paid artist consultations. V2 needed a safer data model, stronger discovery, and a migration path that preserved the trust already earned.

130+legacy accounts migrated

137paid meetings preserved

49Postgres tables across four graph layers

GreenRoom discovery interface connecting professional profiles to collaboration goals — Natural-language goals resolve against structured profiles, credits, relationships, momentum, and intent.

The constraint

V1 stored identity, payments, and booking history across AWS serverless services. A clean rewrite could not erase users, completed consultations, or authentication continuity.

The engineering move

I designed a four-layer Postgres graph model for entities, relationships, momentum, and intent, then built a DynamoDB to Postgres ETL and Firebase authentication bridge.

Why it matters

The product can now answer higher-value discovery questions while keeping legacy customers and transactions intact. AI operates over structured evidence rather than ungrounded profile text.

GreenRoom onboarding importing published projects from connected music platforms — Published work becomes structured profile evidence during onboarding.

GreenRoom professional credits organized by type and source verification — Credits remain typed, searchable, and tied to supporting sources.

GreenRoom V1 remains publicly available while V2 moves toward beta.

Visit GreenRoom V1

Assured GuarantyData engineer

Reducing data onboarding and model calibration from operational bottlenecks to repeatable systems.

85%faster new data-source onboarding

100M+parameter search space distributed

2 hrscalibration runtime after redesign

The system

A self-service Snowflake framework automated schema creation, access controls, and ingestion templates for new sources.

The scale problem

A Python, Kafka, and AWS ECS calibration system distributed grid search that previously ran locally for days or weeks.

The result

Analysts gained repeatable access to firm-wide credit datasets, while model calibration completed in roughly two hours.

Professional experience summarized from public resume material. Internal interfaces and proprietary data are not shown.

Bloomberg LPSoftware engineer, Data Technologies

Moving high-volume financial data without slowing the research workflows built on top of it.

6,500+companies in the ESG migration

45%lower query latency

Globalfinancial data acquisition and delivery

The work

I engineered a Kafka migration for ESG company data used in quantitative research and built normalized bank-filing pipelines for Terminal credit analysis.

The constraint

Data contracts and downstream research behavior had to remain dependable while acquisition and processing systems changed underneath them.

The result

The migration reduced query latency by 45 percent while continuing to serve workflows built around thousands of companies.

Professional experience summarized from public resume material. Bloomberg client systems and proprietary data are not shown.

QuantMechanixIndependent research platform

Making a trading research pipeline resumable, explainable, and hard to bypass.

This is a strategy-research and decision-support platform, not a claim of investment performance.

500 to 30 to 10daily symbol-reduction pipeline

13model ensemble in the current source of truth

9blocking pre-trade checks

QuantMechanix decision desk showing ranked research candidates and model context — The desk compresses a broad daily scan into a smaller review queue with model context and explicit risk checks.

The constraint

Market-data limits, partial failures, and long-running analysis made an all-or-nothing batch unreliable and difficult to audit.

The engineering move

I added Postgres checkpoints, Redis rate limiting, scheduled ingestion, prediction logging, and automated end-of-day outcome settlement.

The risk control

A server-stamped PASS or FAIL gate blocks execution unless all nine checks succeed. The interface exposes uncertainty instead of hiding it.

QuantMechanix conviction panel labeling insufficient track record as unproven — Conviction remains visibly unproven until the evidence threshold is met.

QuantMechanix ranked research queue with direction and conviction context — The queue separates trade-ready research from watch and reject states.

Apply the same discipline

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A three-day Production Triage will identify the highest-leverage technical move.

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