I help businesses build AI-powered applications and scalable full stack platforms — from idea to production. With hands-on experience in Generative AI, SaaS development, and modern web technologies, I specialize in creating systems that are not just functional, but intelligent, automated, and ready to scale. 🚀 What I can help you with: -AI Integration & Development -GPT-based apps, AI agents, chatbots, automation workflows -Full Stack Development -React / Next.js, Node.js, FastAPI, scalable backend architectures -SaaS & Product Development -Multi-tenant platforms, dashboards, subscription systems -API & System Integrations -OpenAI, LangChain, CRMs, third-party tools -Cloud & DevOps -AWS / Azure, Docker, Kubernetes, CI/CD pipelines 💡 My approach: I focus on understanding your business first — then design a solution that saves time, reduces cost, and improves efficiency. Clean code, clear communication, and on-time delivery are standard. Whether you're looking to: -Build an AI MVP -Automate business processes -Scale an existing product -Or integrate AI into your current system …I can help you execute it smoothly. 🤝 Let’s connect If you have an idea or an ongoing project, feel free to reach out — happy to discuss and suggest the best approach. Core Concepts Artificial Intelligence (AI) | Machine Learning (ML) | Deep Learning | Neural Networks | Supervised Learning | Unsupervised Learning | Semi-Supervised Learning | Self-Supervised Learning | Reinforcement Learning (RL) | Transfer Learning | Online Learning | Active Learning | Ensemble Learning | Federated Learning | Meta-Learning | Few-Shot Learning | Zero-Shot Learning | Multi-Task Learning | Curriculum Learning | Contrastive Learning | Representation Learning Classical ML Algorithms Linear Regression | Logistic Regression | Decision Trees | Random Forest | Gradient Boosting | XGBoost | LightGBM | CatBoost | Support Vector Machines (SVM) | K-Nearest Neighbors (KNN) | Naive Bayes | K-Means Clustering | DBSCAN | Hierarchical Clustering | Gaussian Mixture Models | PCA (Principal Component Analysis) | t-SNE | UMAP | Isolation Forest | One-Class SVM | Association Rule Mining | Apriori | Collaborative Filtering | Matrix Factorization | Hidden Markov Models | Bayesian Networks | Gaussian Processes Deep Learning Convolutional Neural Networks (CNN) | Recurrent Neural Networks (RNN) | LSTM | GRU | Transformers | Attention Mechanism | Self-Attention | Multi-Head Attention | Autoencoders | Variational Autoencoders (VAE) | Generative Adversarial Networks (GAN) | Diffusion Models | Graph Neural Networks (GNN) | Siamese Networks | U-Net | ResNet | EfficientNet | Vision Transformers (ViT) | Backpropagation | Gradient Descent | SGD | Adam Optimizer | Batch Normalization | Dropout | Regularization | Activation Functions | ReLU | Softmax | Loss Functions | Cross-Entropy | Embeddings | Encoder-Decoder Architecture LLMs & Generative AI Large Language Models (LLM) | GPT | BERT | Claude | Llama | Gemini | Mistral | Prompt Engineering | Prompt Optimization | In-Context Learning | Chain-of-Thought (CoT) | Retrieval-Augmented Generation (RAG) | Vector Databases | Semantic Search | Embedding Models | Fine-Tuning | LoRA | QLoRA | PEFT | RLHF (Reinforcement Learning from Human Feedback) | DPO | Instruction Tuning | Context Window | Tokenization | Token Optimization | Hallucination Mitigation | Grounding | Function Calling | Tool Use | AI Agents | Agentic Workflows | Multi-Agent Systems | Orchestration | Guardrails | LLM Evaluation | LLM-as-a-Judge | Structured Outputs | Text-to-SQL | Text-to-Image | Multimodal Models | Foundation Models | Model Context Protocol (MCP) | Knowledge Distillation | Quantization | GGUF | Inference Optimization | Speculative Decoding | KV Cache NLP Natural Language Processing (NLP) | Natural Language Understanding (NLU) | Natural Language Generation (NLG) | Named Entity Recognition (NER) | Part-of-Speech Tagging | Sentiment Analysis | Aspect-Based Sentiment Analysis | Text Classification | Topic Modeling | LDA | BERTopic | Text Summarization | Machine Translation | Question Answering | Information Extraction | Information Retrieval | Text Mining | Word Embeddings | Word2Vec | GloVe | TF-IDF | Bag-of-Words | N-grams | Language Modeling | Sequence-to-Sequence | Coreference Resolution | Dependency Parsing | Intent Classification | Entity Linking | Semantic Similarity | Fuzzy Matching | Chatbots | Conversational AI | Dialogue Systems Computer Vision Computer Vision (CV) | Image Classification | Object Detection | YOLO | Faster R-CNN | SSD | Image Segmentation | Semantic Segmentation | Instance Segmentation | Mask R-CNN | SAM (Segment Anything) | Optical Character Recognition (OCR) | Face Recognition | Facial Landmark Detection | Pose Estimation | Keypoint Detection | Image Generation | Style Transfer | Super-Resolution | Image Captioning | Visual Question Answering | Video Analytics | Action Recognition | Object Tracking | Depth Estimation

Seven years building data systems that businesses actually use — from raw API to the dashboard a CEO or teams open every morning. My background is end-to-end: I write the Python pipelines that pull data from your sources, model it in BigQuery using dbt, and build the Looker Studio or Tableau dashboards your team relies on for decisions. I work both sides of the stack because understanding the data underneath is the only way to build analysis you can actually trust. Most recently I spent three years as the sole analytics engineer at Burga, a fast-scaling international DTC e-commerce brand. Every number their marketing, finance, and product teams used — campaign ROI, customer CLV, churn models, financial P&L, A/B test results — came from infrastructure I built and maintained alone. Before that I built the entire data function from scratch at Firebird Tours, and earlier held data and financial analysis roles at Barclays and Western Union. What I can help you with: Looker Studio / Tableau / Power BI dashboards — connected properly to your data, not just to a Google Sheet BigQuery data warehouse — from raw source ingestion through to clean, modeled data your team can query dbt data modeling — staging, intermediate, and mart layers; tested, documented, production-ready Python ELT pipelines — custom API integrations for any source (Shopify, Klaviyo, Google Ads, Facebook Ads, HubSpot, Salesforce, and others) Marketing analytics — attribution, channel ROI, CAC, CLV, cohort retention, A/B test measurement Financial reporting automation — P&L, cost allocation, real-time income statements Data consulting — architecture review, tool selection, data quality audits, fixing what someone else broke Marketing Admin: Google Tag Manager, Tracking setup, Hubspot Automation, Salesforce, HTML/CSS My stack: BigQuery · dbt · Python · GCP (Cloud Run, Vertex AI) · Looker Studio · Tableau · Power BI · Fivetran · Airbyte · SQL · GA4 · Shopify · Klaviyo · HubSpot · Salesforce · Docker · Git I work cleanly — documented code, clear naming, handoff-ready projects. If I build something for you, you own it and can maintain it without me. I communicate clearly and proactively, flag problems before they become surprises, and don't disappear mid-project. New to Upwork, not new to the work. Happy to start with a small scoped task so you can see the quality before committing to something larger. Based in Lithuania (EU) · Fluent English · Available immediately

I work with startups and tech companies that have outgrown their initial data setup. If you're using Snowflake, dbt, or a modern data stack and experiencing performance bottlenecks, rising cloud costs, or architectural complexity, I provide high-level architecture reviews and optimization strategies. Background includes advanced ML optimization research and real-world data platform modernization projects in US-based environments. Let’s turn your data infrastructure into a strategic asset instead of a cost center.

With an MS in Data Science and 7+ years of industry experience, I architect end-to-end data solutions that bridge the gap between theoretical innovation and practical business impact. Core Competencies 🎯 Data Science Leadership 📊 Orchestrate sophisticated data pipelines from extraction to deployment, leveraging advanced statistical methods and machine learning algorithms Drive business decisions through predictive modeling and deep learning implementations using PyTorch, TensorFlow, and scikit-learn Pioneer automated A/B testing frameworks that accelerate decision-making cycles Analytics & Visualization 📈 Craft compelling data narratives through interactive dashboards using Power BI, Tableau, and custom Python visualizations Design real-time monitoring solutions with Google Data Studio and Klipfolio Implement advanced time series forecasting using LSTM, GRU, and ARIMA models Technical Arsenal ⚡ Cloud & Infrastructure ☁️ Azure: Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, Azure Stream Analytics, Azure Functions, Azure DevOps, Azure Cosmos DB AWS: Amazon EMR, AWS Glue, Amazon Redshift, Amazon S3, Amazon Athena, AWS Lambda, Amazon QuickSight, Amazon SageMaker, DynamoDB GCP: BigQuery, Dataflow, Cloud Storage, Pub/Sub, Dataproc, Cloud Functions Data Processing & Storage 🔄 Big Data: Apache Spark, Hadoop, Hive, Delta Lake, Databricks Databases: PostgreSQL, MongoDB, Cassandra, Redis, Snowflake Stream Processing: Apache Kafka, Apache Flink, Apache NiFi Data Quality: Great Expectations, dbt, Apache Griffin Machine Learning & AI 🤖 Frameworks: PyTorch, TensorFlow, scikit-learn, Keras, XGBoost, LightGBM MLOps: MLflow, Kubeflow, DVC, Weights & Biases Experimentation: A/B Testing, Multi-armed Bandits AutoML: H2O.ai, AutoKeras, TPOT Development & DevOps 🛠️ Containerization: Docker, Kubernetes CI/CD: Jenkins, GitHub Actions, GitLab CI IaC: Terraform, Ansible, CloudFormation Monitoring: Prometheus, Grafana, ELK Stack Analytics & BI 📊 Visualization: Power BI, Tableau, Looker, QuickSight, Qlik Sense Python: Pandas, NumPy, Matplotlib, Seaborn, Plotly SQL: Advanced SQL, Window Functions, Performance Tuning Notebooks: Jupyter, Databricks Notebooks, Google Colab Signature Achievements 🏆 Reduced data processing time by 60% through optimized ETL pipelines and distributed computing Implemented ML models achieving 85%+ accuracy in predictive maintenance Architected scalable data warehouses handling 10TB+ of structured and unstructured data 📫 Open to collaborations and innovative data projects 🌟 Azure Certified: DP-203 Data Engineer Associate 🏅 Azure Certified: DP-900 Azure Data Fundamentals #DataScience #MachineLearning #Azure #AWS #Python #DeepLearning #ETL #DataEngineering #BigData #AI #DataVisualization #NLP #TimeSeries #CloudComputing #DataArchitecture #DataAnalytics #BusinessIntelligence #PredictiveAnalytics #DataPipelines #DataModeling #Databricks #Snowflake #MLOps #DevOps #Kubernetes #Terraform #ApacheSpark #DataLake #DataWarehouse #StreamProcessing

I’ve cut over €118,000 a year in operational data costs, and I do the same thing on Snowflake: find where the credits are actually going, then fix the modeling that caused it. Snowflake is where I go deepest, but the work underneath it travels. Most of what I do is ingestion design, incremental modelling and making pipelines survivable in production — I’ve built that on Azure, AWS and Kafka as well. Snowflake-certified, 5+ years building production data platforms. I currently lead a data engineering team running telemetry and anomaly detection on Snowflake across a 35M-device connected fleet, so I’ve dealt with the scale where cost mistakes get expensive fast. Most Snowflake bills aren’t a warehouse-sizing problem. They’re a modeling problem in disguise: unclustered tables getting scanned end to end, auto-suspend left at defaults, incremental jobs quietly reprocessing full history, dashboards hitting raw tables that should have been aggregates two layers back. I diagnose which one you have before changing a single setting. What I take on: • Snowflake cost audits — credit attribution by warehouse, user and query pattern, with a ranked remediation list and estimated savings per item • Data modeling — dimensional and Data Vault, incremental strategies, clustering and partition design • Pipelines — Kafka to Snowflake, Snowpipe, Streams and Tasks, dbt, Python • Cortex — LLM functions and native ML for anomaly detection and forecasting I take a small number of freelance engagements alongside my full-time role, so I’m selective about fit. If you send me your last 30 days of ACCOUNT_USAGE query history, I’ll tell you where the money is going before you pay me anything.

ETL-focused Data Analyst Engineer with 3 years of experience designing data pipelines and analytics solutions on GCP and AWS. Strong communicator with ability to manage end-to-end data projects independently and deliver actionable insights through Python, SQL, and modern BI tools. Skilled in building scalable ETL workflows using Dataform and DBT, and experienced in LookML-based data modeling to power robust self-service analytics in Looker.

Data Engineer and Analytics Engineer with 6+ years building modern, cloud-based data platforms that turn raw data into business decisions. Have lead the BI & Data Engineering function at a fast-scaling fintech in El Salvador. What I do: • Design and build cloud data warehouses on Google Cloud Platform (GCP) and BigQuery • Develop ELT/ETL pipelines in Python, orchestrated with Apache Airflow • Model data using Kimball dimensional modeling and dbt for transformation layers • Deliver self-service analytics through Looker, Looker Studio, Tableau, and SiSense • Integrate APIs and external sources into unified, governed datasets • Apply IAM, lineage, and metadata best practices for data governance Stack I work with daily: Python · SQL · BigQuery · GCP · dbt · Apache Airflow · Looker · Looker Studio · PostgreSQL · MySQL · MongoDB · Tableau · SiSense · Git Industries: Fintech, Telecom, Financial Services, SaaS I've worked with US, LATAM, and remote-distributed teams across TELUS Digital, AML Partners, Brusus, and Manhattan Associates — delivering certified datasets, production pipelines, and dashboards that executives actually use. Open to: Senior Data Engineer, Analytics Engineer, BI Engineer, and Data Engineering Lead roles — remote, LATAM, or US-friendly time zones.

With over a decade of experience and successful collaborations with 30+ startups, I bring a unique blend of deep technical expertise and creative problem-solving to deliver scalable, secure, and user-focused solutions. As a Senior Full Stack Developer, I specialize in building everything from SaaS products and modern e-commerce platforms to AI-driven applications, bridging the gap between business vision and technical execution across front-end, back-end, DevOps, and AI/ML domains. 🛠️ Full Tech Stack Breakdown: 🔍 Back-End Magic: Languages & Frameworks: Node.js (Express, NestJS) | Python (Django, FastAPI, Flask) | Ruby on Rails | PHP (Laravel) Databases: PostgreSQL | MySQL | MongoDB | Redis | SQLite API Design & Integration: RESTful APIs | GraphQL | gRPC | WebSockets Third-party API integrations (Stripe, Firebase, AWS, Twilio, Social Media APIs) 🖌️ Front-End Innovation: Libraries & Frameworks: React.js | Next.js | Angular | Vue.js | Svelte UI & Styling: TypeScript | Tailwind CSS | Bootstrap | Material UI | Chakra UI | SCSS | LESS State Management: Redux | Context API | Recoil | Zustand Build Tools & Bundlers: Webpack | Vite | Babel | ESLint | Prettier 📱 Mobile App Development: React Native | Expo | Flutter (Dart) ☁️ Cloud & DevOps: Platforms: AWS (EC2, S3, RDS, Lambda, CloudFront) | GCP | Azure | Heroku | Vercel | Netlify Containerization & Orchestration: Docker | Kubernetes CI/CD & Deployment: GitHub Actions | GitLab CI | Jenkins | Bitbucket Pipelines Monitoring & Logging: Prometheus | Grafana | ELK Stack | Sentry 🤖 AI / Machine Learning / Data Science: Languages & Libraries: Python | R | Scikit-learn | TensorFlow | Keras | PyTorch | XGBoost | OpenCV NLP & LLMs: spaCy | Hugging Face Transformers | NLTK | GPT (OpenAI API) | LangChain | LLaMA | RAG Data Science & Analytics: Pandas | NumPy | Matplotlib | Seaborn | Plotly | PowerBI Big Data & Cloud AI: Apache Spark | PySpark | Databricks | Google BigQuery | Azure AI Studio | AWS SageMaker Database & ETL: Snowflake | Airflow | dbt | PostgreSQL | MongoDB | Kafka 🔄 Development Workflow: Version Control: Git (GitHub, GitLab, Bitbucket) Agile/DevOps: Jira | Trello | Slack | ClickUp Code Quality & Testing: RSpec | PyTest | Jest | Mocha | Cypress | Postman 🌟 Client Satisfaction: I’ve delivered results to high-growth startups across industries like e-commerce, media tech, fintech, healthcare, real estate, and education. My focus is always on performance, security, scalability, and user experience. Clear communication, reliable delivery, and a proactive mindset are what I bring to every project. 🚀 Ready to Elevate Your Vision? If you're looking for a senior-level developer who can lead and build full-stack applications, integrate AI/ML capabilities, and deliver secure, modern solutions. Let's talk. ✉️ Contact me now, let’s build something great together!

Looker & BigQuery Expert | SwiftUI iOS Apps | Claude AI Automation | Founder @ RavenCoreX Since 2019 I've helped U.S. SaaS, retail & media companies migrate legacy BI stacks to Google Cloud + Looker, cut BigQuery spend by up to 40%, and deliver dashboards 4× faster. In parallel, I've spent 3 years building iOS apps with SwiftUI — from real-time audio transcription to personal finance engines — all shipped to the App Store. I run RavenCoreX, an AI-native tech studio where I build both the data layer and the mobile layer of products. One example: LKMind, a platform for automated Looker + BigQuery analysis using AI agents and Claude. Another: NotarIA, an iOS meeting intelligence app with real-time transcription, Claude-powered summaries, and StoreKit 2 subscriptions — live on the App Store. Credibility at a glance: • 6,353 Upwork hours – 100% Job Success – Top Rated Plus • $100K+ earned delivering end-to-end data platforms • Hands-on with Looker Standard, Enterprise, GCP Core & Studio • 3 iOS apps in production: NotarIA (App Store), BeMoney, FunnyOuts What I deliver — Data & Analytics: 1. Migrations – Redshift / SQL Server / SSRS → BigQuery + Looker (30+ dashboards moved) 2. AI-powered reporting – Claude-generated commentary wired into Looker Studio or Looker dashboards 3. Performance & cost optimisation – PDTs, datagroups, slot tuning (saved $72K/yr for last client) 4. AI agent pipelines – systems that read data context, generate and review LookML/SQL automatically 5. Governance & security – role-based models, CI/CD with Terraform What I deliver — iOS / SwiftUI: 1. Apps from scratch – SwiftUI + Firebase + Claude AI, built to App Store submission 2. AI features – Claude API integration, on-device Apple Translation, AssemblyAI transcription 3. Monetisation – StoreKit 2 subscriptions, RevenueCat, freemium paywalls 4. Backend integration – Firebase Auth, Firestore, Cloud Functions, GCP 5. Real-time features – WebSocket streaming, AVAudioEngine, live transcription Toolbelt: Data: BigQuery • Looker (all editions) • Looker Studio • LookML • dbt • Airflow • Terraform • Python iOS: SwiftUI • Swift • SwiftData • Firebase • StoreKit 2 • Claude API • AssemblyAI • Xcode AI: Claude API • n8n • AI agent orchestration Typical engagements: • 🔍 Looker Health-Check (2 wks) – performance audit + action plan • 🤖 AI Reporting Setup (1–2 wks) – connectors + Looker Studio templates + Claude commentary • 📱 iOS App Build (6–12 wks) – SwiftUI + Firebase + AI features + App Store submission • 🚀 Pipeline Build (4–6 wks) – GCS → BQ → Looker Studio with CI/CD & docs 👉 Click "Invite to Job" or book a paid discovery call (credited toward the project).