We make data easy
Operated by engineers with 20+ years of collective experience, with a focus on financial services. Based in NYC.
Engineering Leads
Managing Partner
Alexander Petralia
Analytics Engineering
Ex-SVP Data Analytics at iCapital. Builds production data platforms and ML products for C-suite stakeholders.
Previously
iCapital
Parth Mehta
AI / ML Engineering
Ex-Meta AI lead. Built Gen-AI image editing and ML-driven ad optimization for eCommerce advertisers. CMU.
Previously
Erica Wu
Data Engineering
Ex-Netflix data engineer. Large-scale batch and streaming pipelines (Spark/Flink) across Netflix, PlayStation, and Apple.
Previously
Tarang Khanna
ML Infrastructure
7+ yrs building ML systems across NLP, vision, and generative AI. LLM training and GPU/Kubernetes infra at Apple, Meta, and Uber.
Previously
Jorge Vicente Cantero
ML Infrastructure
Ex-Netflix ML infra engineer. Co-architected Netflix's real-time ML feature platform. Scala compiler contributor; EPFL.
Previously
Clayton Leach
Data Science
Senior data scientist, ex-Stripe. Risk and fraud modeling across fintech. UC Berkeley (MIDS).
Previously
Berkeley
We modernize yourdata foundation.
Data Transit connects data sources to destinations. Once set up within your cloud, ETL jobs continuously run to extract data from your sources into your data warehouse. Rather than writing your own extractors for every data source, Data Transit systems come with these built-in. We onboard and configure Data Transit so that data continuously and reliably lands in your data warehouse.
Our Preferred Vendors
The Data Warehouse is the single source of truth for your firm's data. Your CRM, billing system, legal drive, and spreadsheets each store a piece of your data, but none is responsible for storing all of it.
We continuously pull data from every source system into the Data Warehouse, where it is reformatted, joined, validated, and reconciled.
This clean, centralized view can then be used for data analysis, fed back into source systems (reverse-ETL), or serve more advanced use cases, such as machine learning.
Our Preferred Vendors
The Analytics Platform turns raw data into useful, actionable insights.
It sits on top of the Data Warehouse, where analysts can build custom reports or run one-off analyses against clean, live data sets.
We work with you to onboard and configure your Analytics Platform to get the most out of your data.
Our Preferred Vendors
Why investin your data?
Track operational KPIs and automate custom workflows.
Our Process
Every engagement starts with a diagnosis, not a build. We assess before we scope, ship in review cycles, and hand the system back to your team. Typical engagement: 90 days to three quarters.
Engagement length
90 days – 3 quarters
Starts with a fixed-scope discovery before any long-term commitment
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30 min · free
Initial call
We learn your systems, goals, and constraints; you see how we work.
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2–4 weeks
Discovery & assessment
Audit the stack, interview stakeholders, and map where data moves revenue or cost.
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~1 week
Proposal & SoW
A scoped plan with milestones, deliverables, and fixed pricing.
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2–4 weeks
Architecture
Target-state platform and system design, reviewed with your team before any build.
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1–2 quarters
Implementation
Built in short cycles — working increments, not a big-bang delivery.
↻ Iterative -
2–4 weeks
QA & handoff
Testing, documentation, and knowledge transfer so your team owns it — no lock-in.
Pre-engagement
Call & paid discovery · ~1 month
Build engagement
Architecture through handoff · 1–3 quarters