DATA ENGINEERING · AI · BANKING · FINTECH · INSURANCE

NapoliData

Data & AI Engineer — Banking, Fintech & Insurance

17 years in data and AI engineering. 12 of them in financial services — credit risk at Banco Patagonia, customer analytics and insurance at Banco Galicia, payments at Prisma.

Independent since 2022, working with clients on AWS, Databricks, and Snowflake.

See experience → LinkedIn → Let's talk
// trajectory
2024 → NapoliData · Independent
2023 — 2024 · Fivvy (Data analytics)
2020 — 2021 · Prisma Medios de Pago
2016 — 2020 · Banco Galicia & Galicia Seguros
2009 — 2016 · Banco Patagonia
17 years · 12 in financial services

17 years in data. 12 in banking, payments, and insurance.

Full trajectory below. Verify against LinkedIn in 30 seconds.
2024 — PRESENT
NapoliData · Independent
Data & AI Engineer. Pipelines, predictive models, and LLM-based automation for clients on AWS, Databricks, and Snowflake.
2024 — 2026
Proactiviti
Consultant Data & AI Engineer. Distributed data pipelines on AWS (Glue, Lambda, Step Functions) and Apache Airflow. Multiple client engagements.
2023 — 2024
Fivvy
AWS Data Engineer. End-to-end data pipelines on AWS for a data analytics startup.
2022 — 2023
Aprende Institute
AWS BI Data Engineer. Analytics infrastructure and BI systems on AWS.
2020 — 2021
Prisma Medios de Pago
Data Scientist Project Leader. Led Big Data & Analytics projects for Argentina's largest payment processing network. Stack: S3-Athena, Python, PySpark, Docker.
2016 — 2020
Banco Galicia & Galicia Seguros
Data Analyst → Senior Data Scientist (Marketing & BI). Built propensity, segmentation, and churn models for Galicia Seguros across life, home, and auto insurance products. In parallel, ran customer analytics for banking products (loans, cross-selling) using RFM and unsupervised techniques, plus NLP for re-marketing chatbots. Measurable lift in sales and material reduction in call-center costs.
Case study overview →
2009 — 2016
Banco Patagonia
Data Analyst (Credit Risk Management). Developed and maintained credit scoring models for retail and corporate clients across six years. Stack: Python, SPSS, SQL.

What I do.

Three areas where I've spent more than a decade solving real problems in financial services.
CREDIT RISK & SCORING
6 years
Design, development, and validation of credit scoring models. Built these at Banco Patagonia for retail and corporate, plus continued work on propensity and risk modeling at Galicia.
CUSTOMER ANALYTICS · BANKING & INSURANCE
8 years · 3 lines
Propensity, segmentation, churn, and recommendation models across life, home, and auto insurance plus banking products (loans, cross-selling). Both classical techniques (statistical, RFM, unsupervised) and modern stack (LLMs, embeddings, RAG).
DATA ENGINEERING · FINANCIAL SERVICES
17 years in data
Production pipelines on AWS, Databricks, and Snowflake. Compliance, auditability, scale. Daily stack: Airflow, Spark, dbt, Delta Lake.

What I write, I keep public.

Production-grade code in public repositories. Review it before you hire me.
DATABRICKS · ETL
Medallion Pipeline
Bronze → Silver → Gold with Delta Lake, PySpark, data quality and optimization.
View repo →
DATABRICKS · DLT
Delta Live Tables
Declarative pipelines with CDC, streaming, and built-in data quality.
View repo →
DATABRICKS · ML
MLflow end-to-end
Feature engineering → training → registry → batch inference in production.
View repo →
DATABRICKS · PERFORMANCE
Cost optimization
Skew handling, partitioning, caching, cost management on production clusters.
View repo →
View all repos on GitHub →

What people say.

References from people I've worked with directly. Available on request.
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See all recommendations on LinkedIn →

Let's talk.

I work on banking, fintech, and insurance projects in three modes:

PROJECT-BASED
Defined scope and timeline. Typically 4–12 weeks.
FRACTIONAL
Senior part-time capacity, embedded in your team.
ADVISORY
Architecture, code review, technical decisions.

Also available for adjacent industries — payments, e-commerce data, and other financial services verticals.

A 30-minute call to see if there's a fit. No pitch.

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