| Tema / Subtema | Jerarquía completa | Comentarios / Detalles |
|---|---|---|
| 📁 1. Databases | ||
| 📁 1.1. Relational | ||
| 1.1.1. Oracle | Managed and maintained Oracle 12c/19c databases, ensuring high availability, performance tuning, and secure backup/recovery. Developed and optimized PL/SQL procedures, functions, triggers, and packages to support business-critical applications. Implemented data migration and replication strategies using Oracle Data Pump and GoldenGate, while monitoring performance through Oracle Enterprise Manager and optimizing SQL queries for efficiency. Collaborated with development teams on schema design, indexing, and best practices, and handled database patching, upgrades, and troubleshooting across production and staging environments. | |
| 📁 1.1.2. MS Sql Server | Managed and maintained MS SQL Server databases (2016/2019), ensuring high availability, performance tuning, and reliable backup/recovery. Developed and optimized T-SQL stored procedures, functions, views, and triggers to support critical applications. Implemented data migration, replication, and ETL processes, while monitoring performance using SQL Server Management Studio and Query Store to optimize queries. Collaborated with development teams on database design, indexing, and best practices, and performed regular patching, upgrades, and troubleshooting across production and staging environments. | |
| 📄 1.1.2.1. SQL Server Reporting Services (SSRS) | ||
| 📄 1.1.3. Informix | ||
| 📄 1.1.4. Postgresql | With PostgreSQL, I’ve designed normalized relational schemas, implemented complex joins and CTEs (Common Table Expressions), and used stored procedures and triggers to handle business logic. A challenging example was optimizing a reporting engine that processed millions of records daily. I refactored queries, created materialized views, and implemented indexing strategies to cut down response time from minutes to under five seconds. | |
| 📄 1.1.5. MySQL | ||
| 📁 1.2. No Relational | ||
| 📄 1.2.1. MongoDB | ||
| 📁 2. AI/ML | Designed, developed, and deployed AI/ML solutions using Python, PyTorch, and Hugging Face Transformers to solve complex business problems. Built and fine-tuned models for NLP, computer vision, and predictive analytics, leveraging frameworks like LangChain and LlamaIndex for retrieval-augmented generation (RAG) and vector databases. Implemented end-to-end ML pipelines, including data preprocessing, model training, evaluation, and deployment, ensuring scalability and performance. Collaborated with cross-functional teams to integrate AI/ML solutions into applications and optimize workflows for real-time inference. | |
| 📄 2.1. LangChain | From 2023 to 2025, developed AI applications in Python 3.10 using LangChain 0.1+ to build advanced language model pipelines, enabling contextual retrieval, multi-step reasoning, and conversational AI solutions. Integrated LangChain with vector databases and external data sources to implement retrieval-augmented generation (RAG) workflows. Designed and optimized prompt engineering, chaining logic, and memory management to improve model performance and response accuracy. Collaborated with teams to deploy LangChain-based solutions in production environments for NLP tasks, chatbots, and intelligent automation. | |
| 📄 2.2. PyTorch | From 2022 to 2025, developed machine learning and deep learning models using Python 3.10 and PyTorch 2.1 for NLP, computer vision, and predictive analytics tasks. Built, trained, and fine-tuned neural networks, optimizing architectures, loss functions, and hyperparameters to achieve high accuracy and performance. Implemented end-to-end ML pipelines including data preprocessing, model evaluation, and deployment to production environments. Collaborated with cross-functional teams to integrate PyTorch models into applications, ensuring scalability, reliability, and real-time inference. | |
| 📄 2.3. LlamaIndex | AI/ML → LlamaIndex | |
| 📄 2,4, Hugging Face Transformers | AI/ML → Hugging Face Transformers | |
| 📄 2.5. Retrieval Augmented Generation (RAG) | AI/ML → Retrieval Augmented Generation (RAG) | |
| 📁 2.6. vector databases | AI/ML → vector databases | |
| 📄 2.6.1. FAISS | AI/ML → vector databases → FAISS | |
| 📄 2.6.2. Pinecone | AI/ML → vector databases → Pinecone | |
| 📁 2.7. LLM-based systems | AI/ML → LLM-based systems | |
| 📄 2.7..1 AutoGen | AI/ML → LLM-based systems → AutoGen | |
| 📄 2.7.2. Evaluation metrics for assessing LLM performance | AI/ML → LLM-based systems → Evaluation metrics for assessing LLM performance | |
| 📄 2.8. OpenAI API | AI/ML → OpenAI API | |
| 📄 2.9. multi-agent workflows that interact via APIs and LLMs | AI/ML → multi-agent workflows that interact via APIs and LLMs | |
| 📄 2.10. ML workflows with Kubeflow | AI/ML → ML workflows with Kubeflow | |
| 📄 2.11. NLP models | AI/ML → NLP models | |
| 📄 2.12. Deploying AI models into applications | AI/ML → Deploying AI models into applications | |
| 📁 2.13. IA Models | ||
| 📄 2.13.1. YOLOv8 | ||
| 📄 2.13.2. LayoutLMv3 | ||
| 📄 2.13.3. YOLOv8 | ||
| 📄 2.13.4. Detectron2 | ||
| 📁 2.14. AI Providers | AI/ML → AI Providers | |
| 📁 2.14.1. Gemini | AI/ML → AI Providers → Gemini | |
| 📄 2.14.1.1. backend integrations and analytics-layer configurations | AI/ML → AI Providers → Gemini → backend integrations and analytics-layer configurations | |
| 📄 2.14.2. Cursor | AI/ML → AI Providers → Cursor | |
| 📄 2.14.3. Windsurf | AI/ML → AI Providers → Windsurf | |
| 📄 2.14.4. Loveable | AI/ML → AI Providers → Loveable | |
| 📄 2.14.5. Vertex AI | ||
| 📄 2.14.6. Pinecone | ||
| 2.14.6. Model Training | ||
| 📄 2.14.6.1.Bedrock | ||
| 📄 2.15. OpenAI Foundry y MosaicML | AI/ML → OpenAI Foundry y MosaicML | |
| 2.16. GenAI | ||
| 📁 2.17. Machine Learning | ||
| 📄 2.17.1. SVM | ||
| 📄 2.17.2. boosting | ||
| 📄 2.17.3. bagging | ||
| 📄 2.17.4. random forest | ||
| 📄 2.18. Deep Learning | ||
| 📁 2.19. NLP | ||
| 📄 2.19.1. NLP Techniques | ||
| 📁 3. Backend Systems | ||
| 📁 4. APP Development/Mobile Development | ||
| 📄 4.1. React Native | ||
| 📄 4.2. .Kotlin | ||
| 📄 4.3. Flutter (Dart) | ||
| 📄 4.4. .NET MAUI | ||
| 📄 4.5. Xamarin | ||
| 📄 4.6. Expo | ||
| 📁 5. Version Control | ||
| 5.1. GIT | ||
| 📁 6. iPaaS | ||
| 6.1. MuleSoft | ||
| 6.2. Boomi | ||
| 6.3. Workato | ||
| 📁 7. Patterns/Architectures | ||
| 📄 7.1. SOLID | ||
| 📄 7.2. Mediator | ||
| 📄 7.3. Pub/Sub | ||
| 📁 7.4. SW Architectures Mobile | ||
| 📄 7.4.1. BLoC | ||
| 📄 7.4.2. Provider | ||
| 📄 7.4.3. Riverpod | ||
| 📄 7.4.4. MVVM | ||
| 📄 7.4.5. MVI | ||
| 📄 7.5. Event-Driven Architecture | ||
| 📄 7.6. GoF | ||
| 📄 7.7. CQRS | ||
| 📄 7.9. Sidecar | ||
| 📄 7.10. DRY | ||
| 📄 7.11. YAGNI | ||
| 📄 7.12. KISS | ||
| 📄 7.13. SLA | ||
| 📁 8. SwiftUI | ||
| 📁 9. Docker/Kubernates | ||
| 📁 10. Cloud | ||
| 📁 10.1. AWS | ||
| 📄 10.1.1. Amazon Bedrock | ||
| 📄 10.1.2. IAM policies or roles to control access | ||
| 📄 10.1.3. AWS Lambda | ||
| 📄 10.1.4. Amazon AppSync | ||
| 📄 10.1.5. AWS Secrets Manager (SS) | ||
| 📄 10.1.6. AWS Key Management Service (KMS) | ||
| 📄 10.1.7. AWS Glue | ||
| 📄 10.1.8. RDS | ||
| 📄 10.1.9. EC2 | ||
| 📄 10.1.10. DynamoDB | ||
| 📄 10.1.11. Elastic Beanstalk | ||
| 📄 10.1.12. ECS | ||
| 📄 10.1.13. CloudWatch | ||
| 📄 10.1.14. SNS | ||
| 📄 10.1.15. SQS | ||
| 📄 10.1.16. SES | ||
| 📄 10.1.17. IAM policies | ||
| 📄 10.1.18. AWS Glue Blob Storage | ||
| 📄 10.1.19. EventBridge | ||
| 📄 10.1.20. API Gateway | ||
| 📄 10.1.21. EKS | ||
| 📄 10.1.22. ECS | ||
| 📁 10.2. Azure | ||
| 📄 10.2.1. Azure Data Factory | ||
| 📄 10.2.2. Azure Databricks | ||
| 📄 10.2.3. Azure synapse | ||
| 📄 10.2.4. Azure Datalake | ||
| 📄 10.2.5. Azure Fabric | ||
| 📄 10.2.6. PENDING | ||
| 📄 10.2.7. PENDING | ||
| 📄 10.2.8. PENDING | ||
| 📄 10.2.9. Azure DevOps Services | ||
| 📄 10.2.10. Azure DevOps | ||
| 📄 10.2.11. Azure Bot Framework | ||
| 📄 10.2.12. Azure Sypnase | ||
📄 10.2.13. Azure Kubernetes Services |
||
| 📁 10.3. GCP | ||
| 📄 10.3.1. Google BigQuery | ||
| 📁 13. MLOps practices | ||
| 📄 13.1. MLflow for model tracking and lifecycle management | ||
| 📁 14. Programming Languages | ||
| 📄 14.1. Java | ||
| 📄 14.2. Go | ||
| 📁 14.3. Python | ||
| 📄 14.3.1. Flask | ||
| 📄 14.3.2. FastAPI | ||
| 📄 14.3.3. Django | ||
| 📄 14.3.4. Pandas | ||
| 📄 14.3.5. PySpark | ||
| 📄 14.3.6. Scikit-learn | ||
| 📄 14.3.7. XGBoost | ||
| 📄 14.3.8. LightGBM | ||
| 📄 14.3.9. Statsmodels | ||
| 📄 14.3.10. Hadoop/Spark | ||
| 📄 14.3.11. TensorFlow | ||
| 📄 14.3.12. NumPy | ||
| 📄 14.3.13. spaCy | ||
| 📄 14.13.14. NLTK | ||
| 📄 14.4. Node.js | ||
| 📁 14.5. Ruby on Rails | ||
| 📄 14.5.1. RSpec | ||
| 📄 14.5.2. Minitest | ||
| 📄 14.6. C# | ||
| 📄 14.7. C++ | ||
| 📁 14.8. JavaScript moderno (ES6+) | ||
| 📄 14.8.1. Node.js | ||
| 📄 14.8.2. Nuxt.js | ||
| 📄 14.8.3. Three.js | ||
| 📄 14.8.4. Next.js | ||
| 📄 14.5.5. TypeScript | ||
| 📄 14.5.6. Angular | ||
| 📄 14.5.7. React | ||
| 📄 14.5.7.1. React JS | In React, I’ve developed complex frontends for SaaS platforms, admin dashboards, and mobile-responsive web applications. My experience includes React Hooks, Redux, Context API, and integrating with backend APIs. I’ve also focused on component reusability, performance optimization, and accessibility. These projects were often part of agile teams in fast-paced environments, including both startups and mid-size tech companies. This role requires working with and PSQL. Can you rate your proficiency with each and mention a complex task you handled using them |
|
| 📄 14.5.7.2. React Native | ||
| 📄 14.9. HTML5 | ||
| 📄 14.10. VB.NET | ||
| 📄 14.11. SQL | ||
| 📄 14.12. R | ||
| 📁 15. Miscellaneous | ||
| 📄 15.1. PENDING | ||
| 📄 15.2. LookML | ||
| 📄 15.3. PENDING | ||
| 📄 15.4. BIQuickSight | ||
| 📄 15.5. ATG Commerce | ||
| 📄 15.6. Webflux | ||
| 📄 15.7. SSR | ||
| 📄 15.8. DDD | ||
| 📄 15.9. Blazor | ||
| 📄 15.10. SignalR | ||
| 📄 15.11. Data Build Tool (DBT) | ||
| 📄 15.12. Powershell | ||
| 📄 15.13. Amazon Redshift | ||
| 📄 15.14. DICOM | ||
| 📄 15.15. DevExpress | ||
| 📄 15.16. Buildkite Integration Pipelines | ||
| 📄 15.17. Certina PSA | ||
| 📄 15.18. Salesforce.com | ||
| 📄 15.19. WebSockets | ||
| 📄 15.20. GraphQL | ||
| 📄 15.21. gRPC | ||
| 📄 15.22. Webrtc | ||
| 📄 15.23. KNIME Server | ||
| 📄 15.24. Bantotal | ||
| 📄 15.25. GeneXus Evolution | ||
| 📄 15.26.. PENDING | ||
| 📄 15.27. Malla | ||
| 📄 15.28. GOAW | ||
| 📄 15.29. Denodo | ||
| 📄 15.30. ODS | ||
| 📄 15.31. Zapier | ||
| 📄 15.32. ServiceNow Automated Test Framework (ATF) | ||
| 📁 116. Crypto exchange environments | ||
| 📄 16.1. Web3 | ||
| 📄 16.2. DeFi | ||
| 📄 16.3. Blockchain Integrations, Smart Contracts, or Wallet services | ||
| 📁 17. APIs | ||
| 📄 17.1 payments, fintech, or financial APIs | ||
| 📄 17.2. Shopify | ||
| 📄 17.3. Salesforce | ||
| 📄 17.4. HubSpot | ||
| 📄 17.5. Swagger/OpenAPI | ||
| 📄 17.6. Stripe | ||
| 📄 17.7. Plaid | ||
| 📄 17.8. Mercury | ||
| 📄 17.9. Optum | ||
| 📄 17.10. Craft Commerce | ||
| 📄 17.11. Global Payments | ||
| 📄 17.12. FreedomPay | ||
| 📄 17.13. Guesty | ||
| 📁 18. Data science/data engineer | ||
| 📄 18.1. Data pipelines for processing large datasets | ||
| 📄 18.2. bigquery | From 2021 to 2025, managed and optimized data analytics workflows using Google BigQuery with SQL and Python 3.10 for ETL, reporting, and business intelligence tasks. Designed and executed complex queries, optimized data schemas, and implemented partitioning and clustering to improve query performance and cost efficiency. Integrated BigQuery with cloud storage, data pipelines, and visualization tools for scalable analytics solutions. Collaborated with cross-functional teams to ensure data accuracy, implement best practices, and support data-driven decision-making across projects. | |
| 📄 18.3. Dataproc | From 2021 to 2025, managed and deployed big data processing workflows using Google Cloud Dataproc with Python 3.10 and Spark 3.x for ETL, data transformation, and analytics tasks. Configured and optimized clusters for cost-efficient, scalable processing of large datasets, and implemented Spark jobs for batch and streaming data pipelines. Integrated Dataproc with BigQuery, Cloud Storage, and other GCP services to enable end-to-end data workflows. Collaborated with data engineering and analytics teams to ensure high performance, reliability, and accuracy of data pipelines in production environments. | |
| 📄 18.4. Power BI | ||
| 📄 18.5. Microsoft Fabric | ||
| 📄 18.6. Microsoft Power Apps | ||
| 📄 18.7. Microsoft DataVerse | ||
| 📁 18.8. ETL Tools | ||
| 📄 18.8.1. Airflow | ||
| 📄 18.8.2. Talend | ||
| 📄 18.8.3. Spark | ||
| 📄 18.8.4. Snowflake | ||
| 📄 18.9. Looker Studio | ||
| 📄 18.10. Tableau | ||
| 📄 18.11. Airbyte | ||
| 📁 19. Testing | ||
| 📄 19.1. NUnit | ||
| 📄 19.2. Selenium | ||
| 📄 19.3. Protractor | ||
| 📄 19.4. Cypress | ||
| 📄 19.5. Playwright | ||
| 📄 19.6. Appium | ||
| 📄 19.7. Capybara | ||
| 📄 19.8. Cucumber | ||
| 📄 19.9. JUnit | ||
| 📄 19.10. Puppeteer | ||
| 📄 19.11. Mochahands | ||
| 📄 19.12. TestCafe | ||
| 📄 19.13. TestRail | ||
| 📄 19.14. Cucumber | ||
| 19.8. RSpec | ||
| 📁 20. Asychrnous Messaging | ||
| 📄 20.1. Kafka | ||
| 📄 20.2. RabbitMQ | ||
| 📄 20.3. ActiveMQ | ||
| 📁 21. CI/CD | ||
| 📄 21.1. Jenkins | ||
| 📄 21.2. GitHub Actions | ||
| 📄 21.3. Azure DevOps | ||
| 📄 21.4. Fastlane | ||
| 📄 21.5. Bitrise | ||
| 📁 22. Security | ||
| 📄 22.1. OAuth 2.0 | ||
| 📄 22.2. OpenID Connect | ||
| 📄 22.3. cifrado TLS | From 2020 to 2025, implemented and maintained TLS/SSL encryption for secure communication in web and backend applications using Python 3.10, Java 11, and OpenSSL 1.1+. Configured TLS certificates, managed key rotation, and ensured compliance with industry security standards (TLS 1.2/1.3). Integrated encryption in client-server communications, APIs, and microservices to protect sensitive data in transit. Collaborated with DevOps and security teams to monitor, audit, and optimize encryption configurations for performance, reliability, and regulatory compliance. | |
| 📄 22.4. Cumplimiento PCI DSS | ||
| 📄 22.5. JWT | ||
| 📁 23. International Standards | ||
| 📄 23.1. ISO 20022 | ||
| 📄 23.2. Open Banking | ||
| 📄 23.3. BCBS | ||
| 📄 23.4. CDD | ||
| 📄 23.5. HIPAA | ||
| 📄 23.6. DMBOK | ||
| 📄 23.7. DAMA | ||
| 📄 23.8. ISO 9000-1 | ||
| 📄 23.9. ISO53:197 | ||
| 📄 23.10. ISO53:193 | ||
| 📄 23.11. HPDH | ||
| 📄 23.12. ISO 27001 | ||
| 📄 23.13. ANSI X12 | ||
| 📄 23.14. EDIFACT | ||
| 📁 24. Electronic Payment Systems | ||
| 📁 25. BPM | ||
| 📄 25.1. Camunda | ||
| 📄 25.2. Appian | ||
| 📄 25.3. Bizagi | ||
| 📁 26. Monitoring Tools | ||
| 📄 26.1. Datadog | ||
| 📄 26.2. Splunk | ||
📄 26.3. Application Insights |
||
| 📄 26.4. Log Analytics | ||
| 📄 26.5. Prometheus | ||
| 📄 26.6. Grafana | ||