Augusto working on his laptop, shown from the waist up.

Analytics Engineer Data & AI

Hey, I'm Augusto!

I make complex data useful.

I build reliable data models and practical AI workflows for Finance, Product, and Marketing.

Explore my work LinkedIn

Currently at Leadtech. Previously Ripio & Diveria.

01 / Professional experience

Data engineering with a business purpose.

Feb 2025 — Present

Leadtech Group

Analytics Engineer

Subscriptions · Remote

One shared payments model. Less repeated integration work.

Unified Stripe, Adyen, and Worldpay data from silver into a shared gold model, standardizing transactions across projects and contributing to an approximately 50% reduction in new-project launch time.

dbtDatabricksData modelingRevenue operations

View Leadtech experienceArchitecture, decisions & outcomes →

Apr 2022 — Feb 2025

Ripio

BI Analyst

Fintech & crypto · Remote

Connecting product behavior to acquisition, retention, and revenue.

Worked with Product, Marketing, and CRM teams to turn attribution, events, and behavioral data into actionable reporting.

Designed and analyzed attribution models across paid and owned channels.
Used Segment and product events for segmentation and customer lifecycle analysis.
Conducted cohort, funnel, and A/B test analyses; built reporting datasets and dashboards.

SegmentAttributionProduct analyticsExperimentation

Aug 2020 — Apr 2022

Diveria

BI Analyst

Cross-industry analytics · Remote

From warehouse foundations to decision-ready dashboards.

Delivered end-to-end analytics projects across e-commerce, real estate, manufacturing, and logistics.

Designed data warehouses using SQL Server and Azure SQL Database.
Built automated ETL pipelines with SSIS and Azure Data Factory.
Created Power BI and Looker Studio dashboards and analyzed customer behavior.

SQL ServerAzure Data FactoryPower BILooker Studio

02 / How I work

From a business question to a trusted answer.

Business understanding first. Reusable models and explicit quality checks throughout.

01
Understand
Start with the decision, stakeholders, and the business definition of a useful metric.
02
Connect
Identify sources, integration boundaries, and the context behind the data.
03
Model
Translate business rules into reusable models and consistent definitions.
04
Validate
Use contracts and validation rules to make quality expectations explicit.
05
Deliver
Make datasets and reporting useful to Finance, Product, and Marketing.

Inside the work

Data → decisions

Different sources.
One shared answer.

Explore a payments modeling workflow.

StripeAdyenWorldpay

01 / Bring the context together

Different providers. One business question.

Stripe, Adyen, and Worldpay describe payments differently. The starting point is understanding each source and the business meaning behind its transactions.

FocusConsistent transaction definitions

02 / Build for reuse

Shared logic, not repeated work.

Bring silver-layer payment data into a shared gold model. Centralize subscription, refund, and revenue logic so new projects can reuse the same foundation.

ToolkitSQL · dbt · Databricks

03 / Make expectations explicit

Trust is part of the model.

Use contracts and validation rules to express what reliable data should look like. Keep business definitions and data-quality expectations close to the models.

FocusContracts & data validation

04 / Put the model to work

A foundation teams can use.

Support Finance reconciliation and reusable reporting. The shared payments model contributed to approximately 50% less time to launch new projects.

OutcomeLess repeated integration work

Based on my work at Leadtech. Illustrative architecture, not live data.

03 / AI in my workflow

AI as a practical engineering tool.

I use AI to extend what I can build and reduce repetitive work—not to replace business understanding or verification.

Reusable skills & MCP

At Leadtech, I built reusable AI skills and MCP workflows that help save approximately 10 team hours per week on repetitive tasks.

Context & delegated work

In Moveler's development workflow, shared agent guidance and specialized skills support scoped implementation and review tasks.

Evidence before confidence

Documented development work combines tests-first corrections and browser validation. AI-generated changes still need human direction and checks.

04 / Personal project

Moveler

A different side of my work: building a digital product end to end.

From the screen to a real place.

A map-first experience for discovering filming locations from movies and series and planning cinematic journeys.

I built the backend, frontend, database, and Stripe integration. It brings product development and AI-assisted engineering together in one personal project.

Explore Moveler