Data engineering services

Usually because of several small gaps in how the team builds and ships data: pipelines without tests, changes that go straight to production, failures that users find before the data team does, and data that nobody owns. Each gap adds some delay and some risk, and together they slow down every new data product.

It is when the problem is getting reliable data built and delivered. If the pipelines work but nobody agrees on what the numbers mean, start with analytics. If you're planning an AI project and don't know what your data can support, start with a data strategy assessment. We can sort this out on the first call.

1. Assessment. In one week I review how your team designs, tests, deploys and monitors pipelines, and where the time goes between a request and a working data product. You get the gaps, ranked by what they cost you.
2. Plan. Recommendations for closing each gap, in the order that pays off fastest, with effort estimates.
3. Gap closure. I work alongside your engineers to close the gaps: adding tests and alerts, setting up automated deployments, restructuring dbt models, or rebuilding the pipelines that fail most. Your team owns the result and the documentation.

The assessment takes one week. The work after it runs anywhere from a few weeks to three months, depending on scope. Adding tests and alerts to a set of existing pipelines sits at the short end. Changing how a team builds and deploys every pipeline sits at the long end.

If you already know the specific problem you want solved, we can skip the assessment, and I scope the project directly.

Mostly AWS, Snowflake and dbt. I work in the stack you already have. Selecting and buying a new tool isn't part of the work.

Client work stays confidential, but StockDataAnalytics is mine to show. It's a production data system I built and still run. Every trading day it loads prices for more than 6,000 stocks, checks each one for 16 chart patterns, scores every setup against more than 370,000 past detections and emails the results before the market opens. An automated pipeline retrains the scoring models and rejects any model that fails on data it hasn't seen. You can see it live at stockdataanalytics.com.

I explain how it works in How to Find High-Probability Stock Setups Before They Break Out (Part 2): how each detection is scored on structure, volume and breakout readiness, and how hundreds of daily detections are narrowed to a short list for subscribers.

The one-week assessment costs a fixed $8,000. The work after it is quoted at a fixed price once the assessment shows what it involves. If you come with a specific problem to solve, I scope and quote that project directly. Either way, you get a fixed quote before any work starts.