I build reliable pipelines
that other engineers don't hate.
3 years designing & optimizing scalable data pipelines and analytics solutions. Currently at Procter & Gamble — building ETL in Python / PySpark / Databricks across Bronze + Silver, shipping Power BI dashboards stakeholders actually open, and enforcing data-quality checks on production pipelines.
David J. Landeo
- status
- ● open to roles
- location
- Madrid, ES
- timezone
- UTC+1 (CET)
- remote
- EU ✓
Key metrics
About & stack
I'm an electronic engineer who became a data engineer. Five years as a teaching assistant at PUCP, an exchange semester in Vigo, hardware roles building drones, air-quality monitors and Peru's first oxygen concentrator — then an MSc in Big Data & AI at the University of Barcelona to pivot into data.
Most of what I do now is unglamorous on purpose: reading other people's PySpark, making it faster, and writing the data-quality checks they wish they'd had. I prefer pipelines that recover quietly to pipelines that look impressive in a slide deck.
Friendliest with Databricks / PySpark / Power BI, comfortable across the rest of the modern data stack, and quick to pick up the next tool when the work calls for it.
Experience
→ present
Data Engineer
→ Feb 2025
AI & Big Data Analyst
→ Jul 2023
Data Engineer · Intern
→ Sep 2022
Electronic Project Engineer
→ Sep 2021
Head of Hardware Area
→ Dec 2020
Technology Development Manager
Education & certifications
→ Jul 2023
MSc · Big Data & Artificial Intelligence Solutions
→ Jun 2019
Student Exchange Program
→ Jun 2019
BSc · Electronic Engineering
Selected projects
Legacy PySpark refactor
Untangled a PySpark codebase nobody wanted to touch — narrower DataFrames, fewer shuffles, cleaner partitioning. Diffs the team could actually review. Runtime dropped from several hours to about one.
Power BI dashboards
Two dashboards tracking pipeline health and business KPIs. Modeled the semantic layer, owned the data contracts, set up alerting.
Contact scraper · RU
Selenium + Sheets/BigQuery/APIs glue and tiny automations that compounded into real hours back to the sales-ops team.
La Mona — silver-jewelry storefront ↗
Full e-commerce site for my mom's artisanal jewelry brand. 77-piece catalog with category filters, a live customizer for real-time engraving preview, and a smart cart that auto-generates WhatsApp orders. Built an offline AI chatbot ("Mona") handling 12+ customer intents — materials, pricing, shipping — zero external APIs. GSAP scroll animations & 3D card tilt. Deployed on Cloudflare Pages. Not data work — proof I ship.
AI-powered app
Wrapper UI on top of an LLM — prompt scaffolding, response formatting, light persistence.
WhatsApp word cloud
Generator that turns an exported chat into a visual word cloud — with stopword cleanup & emoji handling.
Chat sentiment analysis
NLP notebook scoring sentiment across WhatsApp chats over time — who's the optimist?
Used-car price model
ML regression to predict used-car prices from listing features — baseline + feature engineering iterations.
Bedroom-price scraper
Web scraper analysing shared-bedroom listing prices across neighbourhoods — a personal house-hunting tool that escaped.
Beyond data
Teaching assistant
5 years explaining electronics at PUCP. Learned how to translate dense things into "I get it now."
Water polo · National Team
3 years on Peru's NT. Recognized as a qualified athlete. I bring team-sport rhythm to standups.
Student Representative
Elected to the University Assembly at PUCP. Also swam on the university swim team — early lessons in showing up.
Coach
Coached swimming and water polo. A good coach asks better questions than a junior data engineer.
Get in touch
Let's talk about data engineering.
Open to Data Engineer roles in Madrid or fully-remote across the EU. Email is fastest — happy to share a case-study deep-dive if you want one.
[email protected]