David Regalado

AI Time Journal

AI Time Journal is a publishing platform that strives to aware users of and around the world of Artificial Intelligence, growing technology trends, and other industry news. Its final aim is to contribute to steering the development of AI for the benefit of humanity.

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Data Engineering Latam

Data Engineering Latam is the largest, fastest growing, and coolest data community in Latin America whose mission is to promote the region's talent through the dissemination of talks, workshops, study groups, collaborative help and the creation of relevant content.

Founder

Stealth Startup

I spearhead the engineering and infrastructure area, leading the design, development, and maintenance of advanced data processing systems. My focus is on ensuring the robustness, scalability, maintainability, security, and efficiency of technological solutions, anticipating future challenges to address them proactively.

I foster a culture of collaboration and technical excellence among the data science and engineering teams, promoting best practices that drive innovation and a positive work environment.

I report directly to the company founders, explaining the work we do in simple terms.

Key achievements:

- I reduced costs by 90% by moving workloads to a combination of Dataform and Dataflow jobs.
- I introduced modern techniques such as RAG and semantic search to the company.
- Designed and implemented a cloud architecture tailored to the startup's needs.
- Parameterized the product code, enabling efficient customization for multiple clients.
- Spearheaded the adoption of key practices such as code versioning, infrastructure as code (IaC), and disaster recovery procedures, strengthening the organization's resilience and technological responsiveness.
- I delivered a custom industry data model tailored for retailers.

VP of Engineering

Nebula.io

Nebula is a well funded startup that develops cutting edge technology that combines proprietary algorithms with deep learning neural networks, natural language processing, and generative AI to connect talent with opportunity. 

I leveraged Apache Beam on Google Cloud Dataflow for building cost effective streaming and batch data pipelines. Other technologies include MongoDB, Elasticsearch, PubSub, BigQuery, and other GCP services.

Senior Data Engineer

University of Chicago

The University of Chicago is a top 10 U.S. University, with more than 100 associated Nobel Prize winners. 

I taught the Python for Data Science program. Some topics covered: data manipulation and analysis, training, evaluation and deployment of Machine Learning (ML) models. 

4 cohorts.

Data Scientist - Teaching Assistant

Anomali

Anomali is the leading cybersecurity company that develops and provides intelligence-driven cybersecurity solutions to enhance threat visibility, automate threat processing and detection, and accelerate threat remediation. 

I built data pipelines using Scala to serve high concurrency requests that enrich data about suspicious traffic sources in order to classify them as trustworthy or not.

Clevertech

Projects:

• MadHive – Responsible for the implementation of pipelines involving Google Cloud Platform to retrieve terabyte-scale data in sub-second response time. This enabled data scientist to be more efficient when doing exploratory data analysis with data across the country (U.S.).

 • DevOps — I spearheaded the implementation of modules in Terraform for the use of infrastructure as code on the GCP cloud.

In charge of a team of 2.

Senior GCP Data Engineer

Scotiabank

Projects:

• I spearheaded the Scotiabank's Data Hub. I did the architecture and design. Some features: ETL Automation, Data Observability, alerts, monitoring. In charge of a team of 5.

Data Architect

Indra

Projects 

• Responsible for Big Data projects for the IT area. of Telefónica del Perú. In charge of a team of 5.
• Responsible for the B.I. Factory for the I.T. area. of Telefónica del Perú. In charge of a team of 20.

Data Engineer Tech Lead

DWConsulware 

• Construction of campaign variables, risks, financial products, etc. to be used by the analytical models of data scientists.
• Preparation of reports in Excel with macros
• Monitoring and resolution of incidents in the production network

Data Engineer

Projects:	

●	Implementation of the Interbank Data Warehouse
●	Implementation of the Universidad Peruana de Ciencias Aplicadas Data Warehouse
●	Implementation of the Sura Data Warehouse

Senior Business Intelligence Consultant

Hi there!

Thank you for visiting my profile. I am passionate about data & AI. Throughout my 15 years of professional experience I have seen projects of various types, sizes and industries. 

Summary

In my country, I've worked in the biggest companies like:

- Financial Services: Scotiabank, Interbank
- Telecommunications: Telefónica del Perú, Claro
- Consulting: Teradata Perú, Indra, Encora
- Consumer Credit Reporting Agencies: Equifax
- Ad-Tech: Manya.pe

In those companies, I did the whole data lifecycle: data extraction, ingestion, data manipulation, data modeling (normalization, denormalization, etc), data analysis, data quality assessments. In short, the ETL part (data pipelines) + data analysis. 

Achievements: The Data Warehouses/Lakes and dashboards were delivered to my clients, so they could start making data-driven decisions.

These recent years I've been working for U.S. clients as a contractor. Specifically:

- Academics: University of Chicago
- Cyber Security: Anomali
- Ad-Tech: Clevertech
- Startup: Nebula.io

Achievements: Data pipelines delivered and students gratuated from the University program.

Highlight

- Founder at Data Engineering Latam, the largest, fastest-growing data community in Latin America: https://www.linkedin.com/company/data-engineering-latam
- I taught Python for Data Science in the University of Chicago.
- Recognized as Champion Innovator by Google Cloud
- I led teams of 20 people in 3 different occasions.
- Recurrent speaker and jury in Big Data and Cloud Computing events.

Studies

- Systems Engineer from the Technological University of Peru (UTP), tenth superior.
- With a specialization in Data Science from Johns Hopkins University.
- With a specialization in Data Warehousing for Business Intelligence from Colorado University.
- With a specialization in Data Science and Big Data Analytics from MIT.
- With 4 certifications, 15 specializations and 46 Google Cloud Platform digital badges.
- With studies in Artificial Intelligence at UBA-IALAB

Tech stack

Cloud Computing: Google Cloud (GCP), AWS, Azure
Data Engineering: Apache Beam, Airflow, Spark, Databricks, Snowflake, dbt, Hadoop, Hive, Luigi
Databases: BigQuery, Teradata, MongoDB, Elasticsearch, Oracle, SQL Server, PostgreSQL, Mysql
Data Visualization: Power BI, Tableau, Looker, Data Studio
Programming languages: Python, Scala, R
DevOps & CI/CD: Terraform, Docker, Kubernetes, Git, GitHub

Roles 

CTO, CDO, CDAO, Data Science Manager, Data Engineering Manager, Head of Data, Tech Leader, Data Architect, Data Engineer, Data Scientist, BI Consultant.

This was four years ago. Young David studied as if there were no tomorrow.

At the time, I shared this achievement along with this message:

"If you hear a voice within you saying 'you cannot paint,' then paint, and that voice will be silenced."

—Van Gogh

It remains relevant.

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚𝐧𝐭𝐡𝐨𝐬?

Anthos is a hybrid and multi-cloud application platform from Google Cloud that allows you to modernize and manage applications across different environments, including on-premises servers and multiple public clouds like AWS and Azure. It provides a consistent development and operations model by extending Google Cloud's services and engineering practices to these diverse infrastructures, enabling consistent security, governance, and management. Built on Kubernetes, Anthos helps organizations build new apps, modernize existing ones, and run them securely anywhere. 

𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬

✅ Hybrid and multi-cloud consistency: Provides a unified platform for managing applications across different environments, reducing inconsistencies.
✅ Application modernization: Helps you modernize existing applications and build new cloud-native ones more quickly.
✅ Security and governance: Offers robust security features and allows for the implementation of security guardrails and policy enforcement across all your environments.
✅ Unified management: Enables you to view and manage multiple Kubernetes clusters from different locations through a single interface.
✅ Streamlined development: Supports consistent development and operations models, which can speed up deployment and improve quality through features like centralized logging and tracing.
✅ Infrastructure and service management: Provides tools for provisioning infrastructure, managing containers with Kubernetes, and managing the communication between services. 

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☁️🧑‍💻

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I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

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💸📈Unlocking Business Potential with Data & Generative AI ╏ Startup Advisor ╏ Mentor Featured on Times Square ╏ International Speaker ╏ Google Developer Expert

3d isometric version of Lima!!!

(credits: Lesly Zerna & r/aicuriosity for the prompt idea!)

Try Image Prompt 👇

Present a clear, 45° top-down isometric miniature 3D cartoon scene of [CITY], featuring its most iconic landmarks and architectural elements. Use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Integrate the current weather conditions directly into the city environment to create an immersive atmospheric mood. Use a clean, minimalistic composition with a soft, solid-colored background. At the top-center, place the title “[CITY]” in large bold text, a prominent weather icon beneath it, then the date (small text) and temperature (medium text). All text must be centered with consistent spacing, and may subtly overlap the tops of the buildings. Square 1080x1080 dimension.

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I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics.

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David Regalado @thecodemancer_ "Google Gemini Nano Banana Pro" Lima Peru

🤯 This is really an improvement!

I wrote about an earlier version of this image generation model here:
https://lnkd.in/etub9QZ2

Now I'm looking forward writing about this new one.

--
🧑‍💻

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I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

Medium: https://lnkd.in/eNhy6CBB
YouTube: https://lnkd.in/d3Kc6PMc
TikTok: https://lnkd.in/d8RvZ6p5
Threads: https://lnkd.in/e285fJdt
Topmate: https://lnkd.in/eQjH3RWP 
Sessionize: https://lnkd.in/dipdM7Zs
X: https://lnkd.in/ezjm873T
Instagram: https://lnkd.in/eejcaqzE

There is a quiet part of data engineering that we rarely say out loud: The more maintenance a data architecture needs, the more it favors the external firm that built it.

👇

I've seen too many "Vendor Agnostic" architectures that are really just "Job Security" architectures. They really are Rube Goldberg machines.

I wrote my latest piece, "The Art of KISSing in Data Architecture," to push back against this entropy.

It's about the DIY Trap: Why serverless usually beats "managing it yourself" (unless you love waking up at 3 AM).

I break down the principles (and the traps) here:

https://lnkd.in/eiJQkHiv

💬 Do you trust your vendors? How can you make sure they're doing what's best for your business rather than their own? Share your thoughts in the comments.

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🧑‍💻

👍 Like, 🔗 share, 💬 comment, 👉 follow

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

Medium: https://lnkd.in/eNhy6CBB
YouTube: https://lnkd.in/d3Kc6PMc
TikTok: https://lnkd.in/d8RvZ6p5
Threads: https://lnkd.in/e285fJdt
Topmate: https://lnkd.in/eQjH3RWP 
Sessionize: https://lnkd.in/dipdM7Zs
X: https://lnkd.in/ezjm873T
Instagram: https://lnkd.in/eejcaqzE

Young David treated his homework like a quiet way to beat yesterday's version of him. This was four years ago. At the time, I shared this achievement along with this message:

"🛫 Fly high, not so the world can see you, but so you can see the whole world! 🌍"

𝐀𝐧𝐭𝐡𝐨𝐬 𝐰𝐡𝐚𝐭?

Anthos functions as a management platform that extends Google Cloud services to run applications consistently across Google Cloud, AWS, Azure, and on-premises data centers, providing a unified management and operational experience. Anthos is explicitly designed as a strong fit for multicloud environments.

𝐊𝐞𝐲 𝐑𝐞𝐚𝐬𝐨𝐧𝐬 𝐀𝐧𝐭𝐡𝐨𝐬 𝐢𝐬 𝐚 𝐆𝐨𝐨𝐝 𝐅𝐢𝐭 𝐟𝐨𝐫 𝐌𝐮𝐥𝐭𝐢𝐜𝐥𝐨𝐮𝐝

✅ Unified Management Console: Anthos provides a "single pane of glass" in the Google Cloud Console to view, monitor, and manage all your Kubernetes clusters and workloads, regardless of where they are deployed (e.g., AWS, Azure, or on-prem). This significantly reduces operational complexity that typically comes with managing disparate cloud platforms.

✅ Operational Consistency: By standardizing on open-source technologies, primarily Kubernetes (GKE), Istio, and GitOps, Anthos provides a consistent application platform and developer experience across all environments. This means application teams can build and deploy applications once ("build once, run anywhere") without worrying about platform-specific details.

✅ Centralized Security and Policy Enforcement: Security policies and configurations (like role-based access controls and resource quotas) can be automated and enforced uniformly across all clusters using Anthos Config Management. This is crucial for maintaining compliance and a strong security posture in a fragmented multi-cloud landscape.

✅ Avoids Vendor Lock-in: Anthos is built on open standards, which gives organizations the flexibility to choose where to deploy their applications based on business needs (e.g., cost, performance, or data residency requirements) and move workloads between platforms if necessary.

✅ Application Modernization: It helps organizations modernize legacy applications by offering tools like Anthos Migrate, which can automatically transform VM-based workloads into containerized applications that can then run on any supported environment.

✅ Service Mesh for Cross-Cloud Communication: Anthos Service Mesh (based on Istio) helps manage, observe, and secure communication between microservices across different clouds, enabling robust, resilient distributed applications. 

𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬

Anthos is designed specifically to address the core challenges of managing multi-cloud environments, making it a very strong and suitable solution for organizations seeking unified operations, security, and flexibility across their cloud landscape. 

Anthos involves a learning curve and significant enterprise investment, so it's typically best suited for large organizations with complex, distributed environments.

We tend to overcomplicate data stacks for the wrong reasons:

❌ Because it looks good on a resume.
❌ Because a consultant sold a "bespoke" 6-month project.
❌ Because we are afraid of "Vendor Lock-in" so we create "Complexity Lock-in" instead.

I just published "KISSing the Data Architecture" to explore why the simplest solution is usually the most robust one.

I dive into a specific architectural showdown: The Rube Goldberg Machine vs. The Modern Semantic Layer. One requires a team of engineers to keep the lights on; the other just runs.

The goal isn't to be clever. The goal is to build systems that don't give you a nervous breakdown.

Check it out below 👇

https://lnkd.in/eyPsuHcA

--
🧑‍💻

👍 Like, 🔗 share, 💬 comment, 👉 follow

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

Medium: https://lnkd.in/eNhy6CBB
YouTube: https://lnkd.in/d3Kc6PMc
TikTok: https://lnkd.in/d8RvZ6p5
Threads: https://lnkd.in/e285fJdt
Topmate: https://lnkd.in/eQjH3RWP 
Sessionize: https://lnkd.in/dipdM7Zs
X: https://lnkd.in/ezjm873T
Instagram: https://lnkd.in/eejcaqzE

David Regalado KISSing the Data Architecture @thecodemancer_

naas.ai

Leading the development of an open Universal Data & AI Platform to help organizations create powerful ecosystems of AI assistants

- Leveraging ontologies as a unifying field between data, AI models, workflows, analytics, and external systems
- Driving innovation in making data and AI accessible and affordable for organizations of all sizes
- Overseeing product development, business strategy, partnerships and team management

Founder & CEO

Forvis Mazars Group

Advising on projects from strategy to execution for Data & AI Products

- Focusing on creating profitable, auditable, and trustworthy AI solutions for clients
- Providing expert guidance on integrating data and AI technologies into business processes
- Assisting in developing strategies for AI adoption and implementation across various industries
- Collaborating with cross-functional teams to ensure successful delivery of data and AI initiatives

Senior Advisor, Data & AI Services

University at Buffalo

Conducting research on how executable ontologies can serve as a universal method for AI assistants/agents alignment, explainability, and compliance

- Exploring the intersection of philosophy, technology, and business in the context of AI systems
- Contributing to academic publications and presentations in the field of applied ontology and AI
- Collaborating with interdisciplinary teams to advance the understanding and application of ontologies in AI

Research Associate in Applied Ontology, Department of Philosophy

CashStory

Automation services company for business professionals.

- Developed a framework to help professionals approach data, automation & AI through Education and Open source products.
- Trained business professionals on how to take back control over their data and build an infinite solution maker mindset.
- Strived to build a better way to see financial & extra-financial information for wiser decision making, taking into consideration environmental issues & well being as much as cash, profit and value.

Technip

Technip, Paris, France (World leader in project management and engineering in the energy industry | Listed on NYSE EURONEXT CAC 40) 

Corporate Cash Manager, Group Treasury Department, April 2013 – Present  

Group Treasury Cash Management

• Perform daily treasury management and driving global liquidity initiatives
• Monitor Cash flows (by nature and by projects), Debt and Working Capital Requirement actuals and forecasts.
• Drive implementation of new structures to maximize cash available at HQ and minimize operational cash balances 
• Work with the treasury senior management on board/other executive presentations
• Develop sound relationships with banking partners for  Working Capital Management, Financial result optimization
• Act in constant relation with internal constituencies (Strategy, Group Consolidation, Tax, Legal, IT, Compliance)

Strategic Business Systems & Process Management 

• Project Manager/Administrator of the Group Treasury Systems (Diapason / Treasury Management Software, Hyperion / Business Intelligence tool) focusing on the continuous development of new functionalities. and implementation of the TMS
• Define cash management and Treasury system architecture of the future
• Lead cross organizational partnership for proposed treasury and finance strategy
• Develop clear project plans and ownership metrics (KPI’s) to drive accountability and execution throughout the group treasury organization.

Group Cash Manager

ESI Group

ESI Group, Paris, France (Pioneer and world-leading provider in Virtual Prototyping | Listed on NYSE EURONEXT Part C) 

Corporate Cash Controller, Financial Controlling, July 2011 – April 2013 

• Work closely with Board members (CEO, CFO, Director of Financial Operations) to integrate Cash in the financial monitoring of the group: development of Cash-flow reporting process, Balance Scorecard KPI's, bi-monthly cash flow anaysis consolidation, monthly usage during the Board. 

• In depth analysis of the financial impact of French R&D Tax Credit (Crédit d'impôt recherche), Taxes and Working Capital Management

• Build operating excel models at local and group level to challenge Cash forecast direct method

Cash Control Project Manager

Brittany Ferries

Onboard Sales

Schneider Electric

Schneider Electric, Chartres de Bretagne, France (Worldwide energy solution provider | Listed on NYSE EURONEXT Part A) 

Supply Chain Financial and Administrative Intern, Region EMEA and Asia Pacific, March 2009 – June 2009 

• Contact with clients/providers for credit collection (means and delay of payment) 

• Internal logistics control, order entry and invoicing and follow-up on orders in SAP  

• Relationship with logistics companies and shipping agents

Supply Chain Financial and Administrative intern

Comité National Olympique et Sportif Français

Membre du Club France aux Jeux Olympiques de Pékin 2008, Novotel Xin Qiao, Pékin, Chine
Au sein de l’équipe Réceptions/Bar: organisation events officiels & médias, soirées des médaillés, permanence journalière

Volontaire, Jeux Olympiques de Pékin 2008 (Beijing 2008)

Parker Hannifin

Mission in 2 subsidies : New Delhi, Inde (May 2008), Singapore (June 2008)
Objective : realize a qualitative study before the launch of a new product in Asia (focus on India)

Marketing Intern (May 2008 : New Delhi,Inde + June 2008 : Singapour)

⚡️ Building @naas.ai - open source data platform | Stanford LEAD alumni | Data strategy & mentoring

I'm Jeremy Ravenel. I’m a farm boy turned data mercenary and now an entrepreneur, I'm passionate about practical innovation to make our digital lives less chaotic.

I help people and companies create data & AI products that make their work easier and more efficient.

My journey started in finance about a decade ago. I thought I was entering an automated business world, but reality hit when I had to build reports using Excel and VBA. Spending more time producing than analyzing wasn't what I had in mind.

Out of frustration, I learned to code. I discovered Python and Jupyter notebooks, and suddenly, things that took hours were done in seconds. This led me to found CashStory, where we helped big French companies bridge the gap between business and tech teams.

Now, I'm working on something really challenging. At Naas.ai, we're building an open Universal Data & AI Platform. Our goal is to help any organization create their powerful ecosystem of AI assistants; leveraging ontologies as a unifying field between data, AI models, workflows, analytics, and external systems.

But that's not all. I'm also diving into research on Applied Ontology at the University at Buffalo, looking at how we can make AI systems more understandable and trustworthy. I'm also advising companies on Data and AI from strategy to execution with Forvis Mazars.

This mix of research, hands-on platform development, and strategy is something I’ve been dreaming about. I'm feeling very grateful for the opportunity to do what I'm passionate about at the intersection of philosophy, tech, and business.

I think anyone should be able to turn their data into a valuable asset. I try to build bridges to make it happen.

If you're curious about how data and AI can make your work easier and more impactful, let's connect!
Here is my email: jeremy@naas.ai

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