Have you outgrown Excel? Is your data scattered across five disconnected systems? I am your technical partner for custom Python scripts, secure API integrations, and advanced SQL queries that analyze millions of rows without a single spreadsheet crash.
There's a moment when a growing business outgrows its spreadsheets. Your main Excel file takes 5 minutes to open. Software systems don't talk to each other, forcing your team to manually export and import CSVs all day. You have mountains of historical data, but centralizing and querying it to find meaningful trends seems impossible.
That's where I come in. As an AWS Certified Solutions Architect and Data Analyst, I connect your disconnected systems automatically using custom API integrations and Python scripts. I safely migrate your massive, messy files and structure them so they can be queried instantly. I use modern cloud solutions like BigQuery to analyze millions of rows, guaranteeing your business operates on a fast, reliable system capable of limitless growth.
You don't need to know the technical difference between standard servers and BigQuery — that's my job. I handle the complex infrastructure securely in the background so you can focus entirely on running your operations.
From API bridges to cloud migrations, every solution is engineered to eliminate the bottlenecks holding your business back.
Stop manually exporting data. If your software isn't talking, I write custom Python scripts and build secure API bridges that perfectly sync your CRM, ad platforms, and accounting software into one centralized location.
When you have too much data to analyze in Excel, I rely on modern data warehouses. I write highly optimized queries in SQL and BigQuery to structure, analyze, and extract deep insights from millions of rows instantly.
Moving from an outdated legacy system or a messy spreadsheet to a modern CRM? I write custom Python scripts that securely extract, clean, map, and transfer years of historical data so you don't lose a single record.
Custom Python pipelines that automate data collection, cleaning, transformation, and delivery — replacing fragile manual processes with reliable, scheduled workflows that run without supervision.
These projects usually begin when existing tools almost work, but not well enough to support growth, reliability, or scale.
When your CRM, finance software, ad platforms, and internal sheets all hold part of the truth, you need an integration layer that joins them consistently.
If datasets have moved beyond spreadsheet limits, the priority becomes storage, query performance, and stable reporting over millions of rows.
Moving between systems often requires extraction, cleanup, remapping, and reconciliation before data can safely go live in the new environment.
Python jobs, recurring exports, validation scripts, and enrichment pipelines that should run on a dependable cadence instead of depending on manual effort.
The Challenge: A logistics company had entirely outgrown their spreadsheet tracking system. Attempting to analyze 2 million rows of shipping data caused their computers to freeze, halting critical operations whenever the team needed a report.
The Fix: I migrated their historical data out of the messy spreadsheet, stored it centrally, and built a custom query environment using BigQuery to process the calculations at scale.
The Impact: Generating the report dropped from "crashing the system" to completing in under 5 seconds, giving the business the capacity to scale operations effortlessly without any spreadsheet limitations.
If your files exceed 100k rows, take minutes to open, or regularly crash during calculations, it's time to consider a proper data warehouse. BigQuery handles millions of rows in seconds and never crashes. I'll assess your data volume and recommend the right architecture during a free consultation.
Any platform with a REST or GraphQL API — including Salesforce, HubSpot, QuickBooks, Xero, Shopify, Meta Ads, Google Ads, Stripe, and hundreds more. If your software exposes an API, I can connect it to your central data system.
Absolutely. I always work from a copy of your data, never the original. Every migration includes a full verification step that confirms record counts and data integrity before any cutover. Your historical data is fully preserved throughout the entire process.
No. I build every solution with the end user in mind. Whether it's a Python script triggered by a button click or a BigQuery dashboard your team accesses through a clean interface, the technical complexity is hidden. You just see the results.
We document sources, destinations, IDs, refresh rules, dependencies, and failure points before implementation begins.
Python scripts, API connectors, SQL logic, warehouse queries, and migration tooling designed for your environment rather than a generic middleware setup.
Record counts, sample checks, and output validation so the new workflow matches expected business logic before your team depends on it.
Solutions designed so dashboards, KPI reporting, and future automations can be layered on top without rebuilding the core data workflow.
A practical article that shows how collection, transformation, and scheduled processing workflows are structured in Python.
An example of the analytical SQL patterns often used once business reporting moves beyond spreadsheet formulas.
A smaller example of data cleanup and validation work, useful when a larger integration project starts with messy exports.
Once your data pipeline is stable, dashboarding and KPI reporting become much easier to trust, maintain, and scale across the business.