Data Engineer @ Astrafy · Madrid, Spain
Data Engineer, analytics-focused.
Currently building data products at Astrafy across dbt, BigQuery, Airflow, Python, SQL, and Terraform, with a strong focus on modeling, automation, and trustworthy analytics.
100+
dbt models
Across staging and marts in production.
5+
data products
Supporting finance and marketing teams.
10+
stakeholders
Using dashboards and governed metrics.
Current role
Data Engineer at Astrafy
Building end-to-end analytics data products on Google Cloud, with a focus on reliable modeling, governed metrics, and self-serve analytics for business teams.
Positioning
Data Engineer, analytics-focused
I’m a Data Engineer with a background in Mathematics, focused on building reliable, business-facing data products. I like turning messy problems into clean data models, robust pipelines, and analytics systems people can actually trust.
Location
Madrid, Spain
Focus
Modern data stack, governed analytics, business-facing data products, and automation that removes reporting friction.
Selected Projects
Public projects that reflect how I think about data systems, analytics engineering, and business-facing outputs.
End-to-end pipeline combining Chicago taxi trip data and weather signals to produce analytics-ready models and dashboards.
An end-to-end analytics workflow on Google Cloud, including ingestion, infrastructure, transformations, orchestration, testing, and BI output.
ELT pipeline for NBA data using Snowflake and dbt, modeled with bronze, silver, and gold layers for BI consumption.
A Snowflake and dbt pipeline that transforms raw NBA data into structured analytical models for game results, player performance, and team analysis.
What I Work On
The areas where I spend most of my time and energy.
Analytics Engineering
Designing dbt projects, dimensional models, and semantic layers that make analytics trustworthy and scalable.
Business-Facing Data Products
Building dashboards, governed metrics, and reporting flows that help teams make decisions without losing trust in the numbers.
Modern Data Platforms
Working with Google Cloud, Snowflake, orchestration, and infrastructure tooling to support reliable end-to-end data workflows.
Automation & Reliability
Using Python, APIs, CI/CD, and infrastructure as code to reduce manual work and make pipelines easier to operate.
How I think about the work
The part of data engineering I enjoy most is the layer between raw ingestion and a number someone can confidently use in a meeting. I care about that handoff being reliable, understandable, and useful.
A lot of my growth has come from building around the modern data stack: dbt, Snowflake, BigQuery, Terraform, Python, BI tooling, and cloud workflows. I like learning by shipping, tightening the model, and making the next version easier to reason about.
More about me