Andrew C. Madson

Data Analysis Top Voice | Professor | Product Management @ LPL Financial

I'm a Data & AI Evangelist, Keynote speaker, Opensource Advocate, and Professor.

I LOVE transforming the complex into the accessible. My insights combine my multiple master's degrees in data analytics and business management with hands-on leadership experience at enterprise organizations. 


My technical expertise spans data analytics, machine learning, and artificial intelligence, complemented by strong communication skills developed through roles as an adjunct instructor at six universities and keynote speaker. I bridge the gap between technical innovation and practical application, helping organizations unlock the full potential of their data.

DM to collaborate 🙌

AVP - Product Management - Compliance Analytics and Monitoring

The $200K Analytics Engineer: Why This Role Didn't Exist 5 Years Ago (And Why You Need One)

Last week, a startup offered $200K for an Analytics Engineer. Five years ago, this title didn't exist. Now they're commanding senior engineer salaries.

I've watched this evolution from both sides—hiring them and training them. The shift isn't about hype. It's about a fundamental gap that nobody else could fill.

𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐚 $𝟐𝟎𝟎𝐊 𝐫𝐨𝐥𝐞:

Data Engineers built rock-solid pipelines but didn't understand business. Analysts knew the metrics but couldn't scale their SQL models to production-level quality. The gap between raw data and business value became a chasm.

Enter the Analytics Engineer: someone who thinks in both languages.

𝐖𝐡𝐲 𝐭𝐡𝐞𝐲'𝐫𝐞 𝐰𝐨𝐫𝐭𝐡 𝐞𝐯𝐞𝐫𝐲 𝐩𝐞𝐧𝐧𝐲:

𝟏. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐛𝐨𝐭𝐡 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬
→ Translate "increase conversion" into data models
→ Explain why that join explodes costs to executives
→ Bridge engineering and business without a translator

𝟐. 𝐓𝐡𝐞𝐲 𝐨𝐰𝐧 𝐭𝐡𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐥𝐚𝐲𝐞𝐫
→ Single source of truth for revenue, churn, CAC
→ Version-controlled definitions everyone trusts
→ No more "which dashboard is right?" debates

𝟑. 𝐓𝐡𝐞𝐲 𝐦𝐚𝐤𝐞 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝟏𝟎𝐱 𝐟𝐚𝐬𝐭𝐞𝐫
→ Pre-built data marts ready for analysis
→ Documentation that actually exists
→ Self-serve analytics that actually works

𝐖𝐡𝐚𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐢𝐧 𝟓 𝐲𝐞𝐚𝐫𝐬?

• dbt made transformation accessible to SQL users
• Cloud warehouses removed compute constraints  
• Git brought software engineering to analytics
• Companies realized bad metrics = bad decisions

𝐓𝐡𝐞 𝐬𝐤𝐢𝐥𝐥𝐬 𝐜𝐨𝐦𝐦𝐚𝐧𝐝𝐢𝐧𝐠 $𝟐𝟎𝟎𝐊:

→ Advanced SQL (CTEs, window functions, optimization)
→ Data modeling (Kimball, Data Vault, or strong opinions)
→ dbt + Git + CI/CD pipeline mastery
→ Business acumen to challenge metric definitions
→ Communication skills to train entire orgs

𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐞𝐜𝐫𝐞𝐭:

The best Analytics Engineers don't just build models. They prevent million-dollar mistakes and drive ROI by asking: "Are we measuring the right thing?"

I've seen them catch revenue leaks, identify broken attribution, and save companies from betting on vanity metrics.

𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: Your data stack doesn't need another engineer or analyst. It needs someone who makes both sides 10x more effective.

That's worth $200K.

Are you hiring Analytics Engineers, or still trying to make analysts scale?

#DataEngineering #AnalyticsEngineering #DataCareers

Andrew Madson

Your database connector is 𝘚𝘖𝘖𝘖𝘖 1992 😂

And it's costing you. Your analytical pipeline could be 21-38x faster.

The culprit? You're still using ODBC or JDBC for columnar workloads. Apache Arrow benchmarks show the difference is staggering—and it comes down to one thing: conversion tax.

𝐎𝐃𝐁𝐂 (𝟏𝟗𝟗𝟐)
→ Row-oriented C API
→ Built for BI dashboards and legacy systems
→ High overhead: forces row-to-column conversion
→ Works with: Excel, Tableau, Power BI, Crystal Reports

𝐉𝐃𝐁𝐂 (𝟏𝟗𝟗𝟕)
→ Row-oriented Java API
→ Built for transactional apps and Spring/Hibernate
→ High overhead: Java object creation per value
→ Works with: Spring Boot, Apache Spark, Kafka Connect

𝐀𝐃𝐁𝐂 (𝟐𝟎𝟐𝟑)
→ Columnar-first API built on Apache Arrow
→ Built for analytical workloads and modern data tools
→ Eliminates the conversion bottleneck entirely
→ Works with: DuckDB, Polars, pandas, PyArrow, DataFusion

𝐖𝐡𝐲 𝐭𝐡𝐞 𝐬𝐩𝐞𝐞𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞?

ODBC and JDBC were designed when row-based processing made sense. But analytical queries scan columns, not rows. Every time you pull data through ODBC/JDBC into a columnar tool, you're paying a conversion tax—serializing rows, deserializing into columns, over and over.

ADBC skips that entirely. Data stays in Arrow format from database to DataFrame. Zero conversion. Zero wasted cycles.

𝐑𝐮𝐥𝐞 𝐨𝐟 𝐭𝐡𝐮𝐦𝐛:
→ Transactional workloads? ODBC/JDBC still makes sense
→ Analytical pipelines with columnar data? ADBC is the move

𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: If you're running Pandas or Polars queries through JDBC, you're leaving serious performance on the table. That's why I am excited about companies like Columnar, which are making ADBC stable and more widespread.

Still on JDBC for analytics—what's holding you back from switching?

#DataEngineering #ApacheArrow #Analytics

FAANG got you hired in 2015. MANDO will get you hired in 2026.

Meta AI · Anthropic · NVIDIA · DeepMind · OpenAI

These five companies are building the models, the chips, and the infrastructure behind everything happening in AI right now. And they're teaching you what they know — for free!

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐟𝐫𝐞𝐞 𝐜𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐚𝐫𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠:

🔵 𝐌𝐞𝐭𝐚 𝐀𝐈 → Free Llama courses on Coursera. Full professional certificate on building generative AI apps — prompt engineering, fine-tuning, RAG, deployment.
🔗 https://lnkd.in/gCpPKUkB

🟠 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜 → Anthropic Academy. Courses on their API, Model Context Protocol (MCP), Claude Code, AI Fluency, plus cloud deployment on AWS Bedrock and Google Vertex. Certificates included.
🔗 anthropic.skilljar.com

🟢 𝐍𝐕𝐈𝐃𝐈𝐀 → Deep Learning Institute (DLI). Dozens of free self-paced courses — generative AI, CUDA programming, RAG pipelines, robotics — with hands-on GPU-powered labs.
🔗 https://lnkd.in/gRtXsxrk

🔴 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐞𝐞𝐩𝐌𝐢𝐧𝐝 → AI Research Foundations on Google Skills. University-level curriculum on building and fine-tuning language models from scratch. Transformers, modern architectures, hands-on activities.
🔗 https://lnkd.in/g4H97fzd

⚪ 𝐎𝐩𝐞𝐧𝐀𝐈 → OpenAI Academy. Workshops, webinars, and on-demand content — from AI basics to advanced integration for engineers. Certifications rolling out with pilot programs at Walmart, John Deere, BCG, and major universities.
🔗 academy.openai.com

The knowledge gap in AI is real. But the access gap? These companies just closed it.

Which one are you starting with?

#AI #MachineLearning #CareerDevelopment

Data + AI Executive | 250K+ Community Builder | O’Reilly Author | Open Source Advocate | andrewmadson.com

Transformo datos en  📝🧵

Análisis de datos a medida para responder a preguntas clave y respaldar tus investigaciones con resultados claros. Dirigido a investigadores, emprendedores, estudiantes de pregrado y postgrado.

Análisis de datos + metodología de investigación

Universidad             

📉Economía, Estadística,  Política Económica, 📝Planificación Económica, Economía y Marketing, 📚 Metodología de la Investigación, Economía Turística, Modelos Tributarios, Política Fiscal y Política Tributaria.

Docente 

Varias entidades

📊 📈Coordinadora de Proyecto: 
Ministerio de Energía y Minas (MEM), Instituto Guatemalteco de Seguridad Social (IGSS).                                                                                                                               


✏💻 Asistente de Proyecto: Superintendencia de Administración Tributaria (SAT), Programa de Naciones Unidas para el Desarrollo (PNUD)-Fondo de Desarrollo de las Naciones Unidas para la Mujer (UNIFEM)-Secretaría Presidencial de la Mujer (SEPREM) y Programa Mundial de Alimentos (PMA).         


 🧑‍💻💻 Analista: 
Secretaría de Planificación Económica (SEGEPLAN), Superintendencia de Administración Tributaria (SAT), Instituto Nacional de Estadística (INE), Instituto Guatemalteco de Seguridad Social (IGSS).

Analista - Asistente de Proyecto - Coordinadora de Proyecto

MIyPE       

🎂Definición de la planeación estratégica:

diagnóstico, identidad organizativa, 🕗metas-indicadores de gestión, compra de equipo y materia prima, definición de costos y formulación del recetario de productos. En el área de repostería-panadería y otros giros de negocio.

Propietaria Academia  y Consultor

✨ Economista | PhD ☕ 🍩

Experiencia en  . . . 

📉  Formulación de indicadores, análisis económico micro  y macro     
📏 Coordinación y asistencia de proyectos
📚  Capacitación y formación                    
🧑‍🤝‍🧑 Recursos humanos basado en competencias

🛠️ Competencias laborales 
Generación de conocimiento = investigación + creatividad + comunicación escrita y verbal + atención a detalles + curiosidad +  razonamiento

PowerBI   |  SQL Server  |   Minitab     🐍 Python  


𝗪𝗵𝗮𝘁 𝗜 𝗵𝗮𝘃𝗲 𝗱𝗼𝗻𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝗳𝗲𝘄 𝘆𝗲𝗮𝗿𝘀  . . .

🌾He sido consultora en temas económicos, definiendo estrategias y políticas para la gestión empresarial y gubernamental.🪻También laboré  como coordinadora y asistente en la formulación y gestión de proyectos .

🪴Me desempeñé como Docente en Licenciatura y Maestría, impartiendo diversos 𝗰𝘂𝗿𝘀𝗼𝘀  (Economía, Estadística, Metodología de la Investigación y Política Fiscal), en diferentes 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝗱𝗮𝗱𝗲𝘀 (Economía, Administración de Empresas e Ingeniería ). 

🍄En 2021 obtuve el Doctorado en Ciencias de la Investigación
Tesis: Representaciones Sociales de los Impuestos en Guatemala, con la supervisión del Doctor Roberto Molina Cruz.
El estudio realiza la primera aproximación de la cultura tributaria, desde la psicología social, tomando como base la teoría de las representaciones sociales, conceptos económicos y de política fiscal. Se utilizó minería de textos y modelos estadísticos. Podéis ver un extract 🥝 https://sites.google.com/view/evelyn-orti/tesis 


Otros temas de interés:   Economía del Comportamiento 😐 e Innovación. 

Me puedes contactar en: eveortiz10@yahoo.com 
                                         
 . . . 🕺  🕺  economía  📈  analytics  | economy  | economics  | estadística | análisis de datos  
#andoendatos #endatosando #economista #capacitación #formación #estadistica

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