Have you outgrown Excel, or is your data split across systems that do not talk to each other? I build Python workflows, API integrations, and SQL reporting systems around the volume and decisions involved.
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 systems with custom API integrations and Python scripts. I migrate and structure data so teams can query it without forcing large workloads through spreadsheets. For larger datasets, I use services such as BigQuery and design the tables and queries around the reporting need.
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, each solution starts with the systems, reporting needs, and checks your team already uses.
I build Python workflows and API bridges that move selected CRM, advertising, and accounting data into a reporting destination on a defined schedule.
When a reporting workload has moved beyond Excel, I use SQL and BigQuery to structure the data and design queries around the questions the team needs to answer.
Moving from a legacy system or spreadsheet into a CRM requires extraction, cleanup, field mapping, reconciliation, and a rollback plan. I build those steps around the source and destination formats.
Custom Python pipelines can collect, clean, transform, and deliver data on a schedule, with logging and review steps for failures or unusual records.
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 real estate company needed to analyze 1.5 million property records stored across mixed-format CSV files. Spreadsheet queries took minutes and were difficult to maintain.
The Fix: I moved the historical files into BigQuery, normalized the fields, and rebuilt the measures the client used for analysis.
The Impact: The same reporting queries now run in seconds instead of minutes. See the homepage case summary.
If your files take minutes to open or regularly fail during calculations, it may be time to move the reporting workload into a database or data warehouse. I will assess the data volume, query patterns, and reporting needs before recommending an architecture.
I work with REST and GraphQL APIs, including Salesforce, HubSpot, QuickBooks, Xero, Shopify, Meta Ads, Google Ads, and Stripe. The first step is checking the platform's API, permissions, limits, and available fields.
I work from a copy of the source data and verify record counts and required fields before cutover. The migration plan also defines backups, rollback steps, and how historical records will be checked.
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.
I 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.