— Code · Design · CoworkTurning messy data into products teams can trust.
Senior Data Engineer with 5+ years designing and delivering end-to-end data platforms across fintech, e-commerce, and enterprise environments — fast, cost-efficient pipelines on AWS, Snowflake, Spark, dbt & Python that turn slow, legacy processes into trustworthy data products for BI, Data Science, and Operations.
Leading the modernization of a business-critical platform — migrating ELT from Spark-based AWS Glue to Airbyte + dbt + Redshift, integrating ~10 GB/day from Oracle, SQL Server, OpenEdge & IBM DB2.
Re-engineered legacy Spark pipelines to eliminate memory bottlenecks — cutting a critical pipeline from 8 h → 30 min 16× faster.
Replaced legacy SSIS packages with Airflow DAGs for observability, maintainability & scheduling control.
Rolling out Claude — skills, agents & MCP servers as a developer-productivity and incident-response accelerator across dbt, Terraform, SQL & Airflow.
Consolidated large on-prem & Amazon RDS Oracle databases into Snowflake — including ~3 TB tables · 50M+ rows/day — via ETL, ELT & Reverse-ETL.
Redesigned the data model from scratch (~20 models across ~50 tables) — reporting 10 h → 5 min.
Built Snowflake stored procedures and high-volume load patterns with Snowflake + Airflow + Airbyte; enhanced pipelines with SnowPark via Notebooks, Worksheets & Streamlit.
Built end-to-end pipelines for a conflict-of-interest application — ~10 sources at ~40 GB/day into a .NET app.
Architected the data layer with AWS Neptune (entity-relationship logic) and OpenSearch (fast per-company lookups).
Implemented a data lake with Apache Spark + Iceberg on AWS Glue & Athena; orchestrated with Step Functions, Glue & Lambda. Earlier: documented IBM DataStage ETL and automated XML extraction in Python.
Built data marts in Snowflake for BI, Data Science & Operations — ~60 GB/day · peak ~150M rows.
Migrated batch ingestion from Apache NiFi to Spark on AWS Glue — 6 h → 20 min.
Built ETL/ELT for the legal & regulatory reporting behind RappiPay's banking-license process in Colombia — contributing to a new bank that remains in operation today. Added Apache Kafka streaming for near-real-time statements.
Built Python, R & Bash geospatial pipelines — a census-cleaning script that did in 1 day what took weeks, and COVID-19 automation 15 h → 15 min.
Developed on-demand geospatial tooling for internal clients and supported ArcGIS / ArcGIS Online through demos and technical talks.
The stack I build on.
From maps to data products.
My path into data started with maps. My thesis applied machine learning to satellite radar (SAR) imagery — the first time I saw what these methods could really do, and what pulled me out of pure GIS toward data. I went looking for a data science role, landed in data engineering instead, and that accident became the work I care about most.
What actually drives me isn't a tool or a title — it's solving hard problems and shipping solutions that create real value for people. Over the years I've worked as an analytics engineer as much as a data engineer: translating stakeholder needs into governed dbt models, enforcing integrity and clear SLAs, and building dashboards teams can self-serve. The pipelines matter, but they're the supporting layer — the important part of a data engineer is the data, not the engineer. The deliverable is trustworthy data products people can act on.
That foundation matters more than ever with AI: the better the data products underneath, the better AI works on top of them — reliable, well-governed data is what turns a model into something you can actually trust. I believe AI, done responsibly, can genuinely improve lives — especially in Latin America and communities that have historically received technology built elsewhere rather than helping shape it. I build with agentic AI every day, and I want that leverage to reach far beyond a handful of companies. Helping build that change, not just use it, is the work I think can make a real difference.
Away from the data platforms, I'm happiest in motion — traveling, hiking, and training CrossFit and running. I still love maps, and lately I'm learning photography.
Books I recommend.
A short shelf on stoicism, mindset, and the practice of staying present.
Meditations
StoicismEgo Is the Enemy
HumilityThe Power of Now
PresenceMindset
GrowthThe Courage to Be Disliked
FreedomBlitzscaling
ScaleNexus
InformationThe One Thing
FocusThe Stoic Virtues
Business 101 for the Data Professional
LiteracyEyes on what's next.
Beyond the data stack, I follow the frontiers that will reshape how we live and work — from autonomous machines to the expansion of human presence beyond Earth. The same mindset that drives good engineering drives curiosity about what comes next.
Humanoid and industrial robots moving from labs into real supply chains. I track embodied AI, locomotion breakthroughs, and the data infrastructure these machines will demand at scale.
Return to the Moon, Mars ambitions, reusable launch vehicles — science fiction becoming engineering timelines. Mission telemetry and deep-space data pipelines are among the hardest problems in the field.
Fusion, next-gen batteries, smart grids. Energy is foundational infrastructure, and the transition to cleaner systems generates genuinely hard real-time data problems worth solving.
Qubits, superposition, and entanglement are moving from physics papers into early commercial hardware. Quantum's implications for cryptography, optimization, and eventually data processing make it one of the most consequential bets of the decade.