About Lopez Data Works

A portfolio brand for data systems that solve real operational problems.

Lopez Data Works is the professional portfolio of Jayson Lopez. It represents the idea that data should not just exist in files, dashboards, exports, and databases — it should work inside the business process.

This portfolio is built around real systems: pipelines, reporting models, automation utilities, operational dashboards, workbook engines, APIs, and decision-support tools created from actual business friction.

30+
Systems built

Data pipelines, dashboards, workbooks, APIs, scanners, pricing engines, and automation tools.

8
Flagship case studies

Selected builds that show deeper architecture, business logic, and operational value.

Proof
Built systems

A curated record of real builds, architecture decisions, operational outcomes, and the story behind the work.

Who I am

I’m Jayson Lopez, a data analytics and data engineering professional focused on turning operational friction into systems that work. My portfolio is built around the practical side of data: the logic, pipelines, validations, dashboards, automations, and delivery layers that help teams make better decisions.

The story

I build from the place where business work gets messy.

My work usually starts where a team is dealing with scattered files, manual reporting, inconsistent exports, repeated copy/paste work, disconnected systems, or decisions being made without enough visibility.

I turn that friction into systems: data pipelines that refresh, dashboards that explain, workbooks that calculate, APIs that expose, scanners that capture, and automations that deliver the output to the right people.

The common thread is simple: make the data usable, reliable, and decision-ready.

01

I start with the operational problem.

Before choosing a tool, I look at the decision being made, the data being trusted, the people doing the work, and the friction slowing the process down.

02

Then I design the system around the workflow.

That may become a pipeline, a Power BI model, a workbook engine, a SQL layer, an API, a scanner flow, or an automation service.

03

Then I make the output usable.

The goal is not only to process data. The goal is to create something people can review, trust, repeat, and use to make a better decision.

What I build

Systems that connect data, process, and decision-making.

The work sits across data engineering, analytics engineering, BI, operational automation, inventory intelligence, procurement, pricing, e-commerce analytics, and app/API development.

Data engineering

API extraction, file ingestion, SQL loading, staging patterns, incremental processing, state tracking, and repeatable ETL workflows.

Analytics engineering

Modeled data, semantic logic, validation rules, metric definitions, executive-ready summaries, and decision-focused outputs.

BI & dashboards

Power BI, DAX, operational KPIs, inventory intelligence, procurement views, pricing analytics, and performance reporting.

Automation systems

Python workflows, workbook generators, email dispatchers, scanners, webhook receivers, and audit-ready process automation.

Principles

How I think about the work.

Business problem first, technology second.

Build systems that can be repeated, audited, and explained.

Make messy operational data usable without hiding the complexity.

Design outputs for the person making the decision.

Protect confidentiality while still showing the architecture and thinking.

Treat every project as proof that data can become operational leverage.

Domains

Where the systems show up.

The portfolio shows how the same system-building approach can be applied across different operational environments.

Inventory intelligenceProcurement and replenishmentPricing and profitabilityCCC / PartsTrader workflowsOrderTime automationPower BI semantic modelsE-commerce analyticsAPI and app development

The brand meaning

Lopez Data Works means the data has to do something.

The name is intentional. It is not only a label for a portfolio — it is the standard behind the work. The system should reduce confusion, expose the right signals, create repeatable outputs, and help the business move with more confidence.

Build the workflow
Structure the data
Validate the output
Make it reusable