AlexisPrado Caceres

1. Map the workFind out how the process really runs, and where time and errors build up.
2. Measure itAgree the definitions with the people who own the numbers.
3. Fix the dataClean and connect sources in layers, so every figure is traceable.
4. Model and reportBuild the model and the dashboard on top of trusted data.
5. Automate and hand overRemove the repeat work and document it so the team can own it.

Projects

Two tracks, one habit: start from the business question. Analytics is what people see. Engineering is how the data gets there.

Illustration. Client data is confidential.

Delivered, in use

Process Area Dashboard

Caja Arequipa, 2022 to 2024

Operational and executive reporting for three departments, built so that every figure meant the same thing to everyone reading it.

• Agreed metric definitions with each department head before building anything
• DAX measures for SLA compliance, cycle time and productivity
• Data from SharePoint cleaned with Power Query and SQL, with scheduled refresh
• Adopted as the organisation's standard monitoring view

Power BIDAXPower Query (M)SQLSharePointPower Automate

Illustration. Company data is confidential.

Delivered, in use

Marketing and operations performance

Bee Products, 2023 to 2025

Weekly view of leads, conversion, ROAS and revenue for management, built on the layered data model that replaced the company's scattered Excel files.

• Raw marketing and sales records moved into bronze, silver and gold layers
• Reporting time cut by 80%, with lineage from source record to report
• Historical audit trail that management didn't have before
• Role-based, least-privilege access across every reporting layer

Power BIPower QueryPythonMedallionPower Apps

Event poster, Data Wizard community

Delivered, March 2026

Power BI "From Zero to Ninja"

Guest speaker, Data Wizard community

The report I built and used to teach Power BI from the basics to a finished management dashboard, in a live online session for a Latin American data community. Use the arrows below to walk through it.

• Medallion approach to organising sources before modelling
• Multi-source transformation in Power Query, basic to advanced
• DAX and M explained through the same report, step by step
• Report design choices that help people make decisions

Power BIDAXPower QueryData modellingTeaching

Workshop demo report built on a public sample dataset

‹ ›

Experience

Data roles in Peru, then consulting in Australia. Alongside them, hospitality work in New Zealand and Queensland that taught me how operations run on the ground.

1. 2026 to nowQueensland, Australia

   Data Automation Consultant

   Self-employed (ABN), regional solar installation client

   • Mapped CRM API field codes to business labels to define one data model across sources
   • Building a Python pipeline for CRM project data and STC compliance records
   • Built and deployed an n8n workflow that reads leads from four providers and replies automatically
2. May 2023 to Jan 2025Arequipa, Peru (part time)

   Data Engineer and Project Assistant

   Bee Products

   • Replaced manual Excel workflows with a Medallion data architecture, cutting reporting time by 80%
   • Python scripts and mobile shortcuts to automate data capture, traceable from source to report
   • Power BI dashboards for operations and management; small Power Apps on Dataverse and SharePoint lists
3. May 2022 to Jan 2024Arequipa, Peru

   Process and Data Analyst

   Caja Arequipa

   • Power BI dashboards for three departments, with metric definitions agreed with department heads
   • Power Automate flows connected to SharePoint and Teams, cutting manual reporting by 30%
   • Mapped workflows in BPMN (Bizagi, Camunda) to find waste and support restructuring proposals

Tools I work with

Grouped by what they're for, not by how long the list can get.