Andreas “Andy” Ranitzsch · Buenos Aires, AR

SSr. Data Analyst & Industrial Engineer AI Automation - Data Governance

3+ yrs working with data to ship insightful products, powerful end-to-end automations, and ensure high quality data.

Currently at Moni: 10+ key dashboards, 1k+ tests on 1.4k fields and tables, RAG implementations and AI automation reducing 50% of time.

  • Languages
  • EducationIndustrial Eng. UBA, ARALEARG Scholarship at LUH, DE
  • fieldsData Eng · Analytics · AI Automation · Data Governance
LinkedIn → GitHub → See work
Portrait of Andreas Ranitzsch
01 · Profile1 / 7

.profile

What I do day-to-day, and what I optimise for.

profile

Industrial Engineer with 3+ years of experience in Data Analytics, Governance and AI applied to Fintech and Crypto. I combine business understanding with advanced SQL, data-quality frameworks (dbt / Elementary), cataloging and lineage, and governance of AI solutions (Cursor, Claude, RAGs, skills and MCPs).

I build prototypes and automations (n8n, agents) under Data Mesh principles, prioritising efficiency and compliance. I adapt quickly, working across technical, product and business teams to drive Data & AI Literacy.

  • Govern — dbt, Elementary, catalog & lineage, Data Mesh
  • Build — SQL on Redshift / PostgreSQL, Python, Tableau
  • Automate — n8n, Cursor, Claude (RAGs, skills, MCPs)
  • Speak — 🇺🇸 C2 · 🇩🇪 C2 · 🇦🇷 native
02 · Stack2 / 7

.stack

Daily drivers in bold; rest is supporting.

01

Data & SQL

PostgreSQLRedshift
02

Modeling & Quality

dbtElementary
03

BI & Viz

TableauPower BILookerMetabase
04

Automation

n8nLangGraphZapiercron
05

Python & ML

Pandasscikit-learnXGBoostPCA
06

Cloud

AWS SageMakerS3LambdaEC2
07

AI & LLMs

ClaudeCursorRAGMCP
08

Other

TypeScriptGit / GitLabScrum
03 · Work3 / 7

.work

Three pillars shipped at Moni — what was built, the metrics, the business move.

01 · dbt Data Governance · 2024 → present

Trust layer for the warehouse

Problem

Fragmented warehouse: no catalog, no lineage. Four product schemas modelled inconsistently. Documentation went stale within weeks. Analysts spent hours reconciling numbers across BCRA reports and credit dashboards before they could act on them.

Solution & Tools

Stood up dbt + Elementary on Redshift / PostgreSQL under Data Mesh principles. Built catalog & lineage from source. Generic, singular and contract tests on every mart; freshness & anomaly checks on revenue / risk tables.

Key Metrics
  • 1k+ data quality & governance tests live
  • 1.4k+ fields and tables in catalog & lineage
  • 4 schemas modelled under Data Mesh
  • Freshness & anomaly alerts on revenue + risk marts
Impact

Bad data caught in CI before it lands in Tableau. Analysts stop reconciling numbers across BCRA reports and credit dashboards — one definition, one number. Audit and regulatory questions answered from lineage in minutes, not days.

dbtElementaryRedshiftPostgreSQLSQLData Mesh
02 · AI Automation · 2024 → present

Flows that retire manual work

Problem

Reporting eaten by manual file formatting, Slack copy-paste and stale docs. Senior analysts losing weekly hours to repeatable tasks. The BCRA regulatory file compiled by hand each night. Onboarding took weeks because docs lagged the warehouse.

Solution & Tools

n8n cron + webhook flows wiring Redshift, the Slack API, Telegram bots and the BCRA file format. Claude RAGs / skills / MCPs + Cursor as a documentation pipeline for Finance, Ops & Mgmt. Data & AI Literacy sessions to put it in everyone's hands.

Key Metrics
  • 8 n8n automations in production
  • 20+ hrs/week reclaimed across Finance, Ops, Mgmt
  • 80% of documentation work accelerated via RAGs / MCPs
  • Nightly BCRA regulatory file + Slack diff (was manual)
Impact

Reporting, alerts and onboarding docs run themselves. Analysts spend the week on analysis, not file formatting and copy-paste. Documentation stays current with the warehouse instead of going stale within weeks — senior time freed for higher-leverage work.

n8nClaude (RAG / MCP)CursorPythonSlack APITelegram
03 · Data Analysis · 2024 → present

Dashboards for revenue & collection

Problem

Revenue, mora (debt collection) and credit-scoring numbers told different stories in different tabs. No shared definitions; no shared lineage from data to dashboard. Risk and finance debated which number was right before they could act on it.

Solution & Tools

Designed and shipped Tableau dashboards on top of the dbt marts: revenue, payments funnel, mora (debt collection), credit scoring outcomes and the BCRA regulatory view. One set of definitions, sourced from the catalog, used by exec, ops and risk.

Key Metrics
  • 10+ Tableau dashboards in daily use
  • BCRA regulatory + C-level credit scoring boards
  • Mora · payments · funnel views for ops & risk
  • Definitions sourced from catalog — numbers match across teams
Impact

Revenue and debt-collection conversations happen on one source of truth. Risk team prioritises mora cohorts faster; finance reads BCRA exposure live; exec sees scoring outcomes without analyst hand-holding.

TableauSQLRedshiftdbt

Diagrams are simplified architecture sketches, not production screenshots. Live dashboards, dbt repo and n8n flows shareable on request.

04 · Projects4 / 7

.projects

Side builds and experiments — what I make on my own time.

andyranitzsch.github.ioHTML / CSS / JS

This portfolio site

Vanilla single-file portfolio styled after the Microsoft Adventure boot screen on a 1981 IBM PC — phosphor green on black, scanlines, ASCII command-rain, ⌘K palette. No build step; deployed via GitHub Pages.

Live site →  ·  Source on GitHub →

HTMLCSSJavaScriptGitHub Pages
05 · Experience5 / 7

.experience

Four roles across fintech, automotive ML and crypto. Moni pillars above.

Apr 2025 → present Buenos Aires, AR Hybrid Fintech · Credit

SSr. Data Analyst at Moni Online

  • Data Mesh on 4 schemas; dbt + Elementary, 1000+ governance tests, catalog & lineage on 400 tables / 1k+ fields.
  • RAGs / MCPs in Claude accelerated 80% of documentation work; 8 n8n automations reclaim 20+ hrs/week.
  • 10+ Tableau dashboards for C-level (BCRA regulatory, credit scoring, mora). Credit-scoring models in Python on SageMaker.
dbtElementaryRedshiftPostgreSQL Tableaun8nClaudePythonSageMaker
Aug 2024 → Apr 2025 USA · remote Remote Crypto · DeFi

Jr. Crypto Data Analyst at Everclear

  • Built Power BI dashboards for marketing & support analytics across the protocol.
  • Wrote a TypeScript API adapter for Defillama feeding internal liquidity views.
  • Shipped Python LLM bots for Telegram — translation & community-adoption content.
Power BITypeScriptPython DefillamaOpenAI API
Oct 2023 → Jul 2024 Hannover, DE On-site Automotive · ML

Data Science Intern at Volkswagen

  • Led a predictive fault-detection system in Python with XGBoost ensembles — reduced production-line downtime.
  • Built an end-to-end ML pipeline: PCA → Isolation Forest → XGBoost on industrial sensor data (scikit-learn / pandas).
  • Automated ETL + Power BI reporting on plant SQL feeds for management.
PythonXGBoostscikit-learn PCAIsolation ForestPower BI
Jun 2021 → Sep 2023 USA · remote Remote Crypto · DAO

Crypto CX Analyst at Connext LLC

  • Maintained KPI dashboards tracking bridge volume, support load and CSAT.
  • Operated blockchain CRM support workflows across multiple L2s.
  • Co-authored DAO grant proposals and ran financial planning for a 12-person team.
SQLExcelCRM DAO GovernanceFP&A
06 · Q&A6 / 7

.q&a

For recruiters and AI engines — the short answers.

Who is Andreas Ranitzsch?

Andreas (Andy) Ranitzsch is an Industrial Engineer and SSr. Data & Governance Analyst at Moni in Buenos Aires, with 3+ years across fintech, automotive ML and crypto.

Where is Andy based?

Buenos Aires, Argentina — working hybrid at Moni.

What is Andy's stack?

SQL on Redshift and PostgreSQL, dbt and Elementary for modeling and quality, Tableau and Power BI for BI, n8n and LangGraph for automation, Python with pandas/scikit-learn/XGBoost, and Claude with Cursor, RAGs and MCPs for AI workflows.

What does Andy do at Moni?

He leads Data Mesh adoption across 4 schemas, runs 1000+ data quality and governance tests on 1.4k fields and tables, ships 10+ Tableau dashboards, and builds RAG/MCP automations that cut process time by ~50%.

What languages does Andy speak?

English at CEFR C2, German at CEFR C2, and Spanish as a native speaker.

What did Andy study?

Industrial Engineering at Universidad de Buenos Aires, with an ALEARG scholarship year at Leibniz Universität Hannover in Germany.

How can recruiters reach Andy?

On LinkedIn (/in/paranitzsch) or on GitHub (@andyranitzsch).

07 · Contact7 / 7

.contact

Reach out.

Email via LinkedIn → LinkedIn /in/paranitzsch → GitHub @andyranitzsch →