I’m an AI Systems & Data Engineer with an M.S. in Data Science from Johns Hopkins. I build production AI and data infrastructure: RAG pipelines, agent workflows, ETL systems, vector databases, analytics dashboards, and cloud data platforms that turn messy operational data into reliable software. My work sits at the intersection of AI, data engineering, data analytics, and applied data science. I can help design the pipeline, clean and model the data, build the dashboard, explain the business story, and connect the system to AI tools like RAG, semantic search, LLM workflows, Pinecone, OpenAI, Claude, Postgres, Snowflake, AWS, or Azure. In practice, that means I work across the full path from raw data to insight to production application. Recent work: • Architected a Postgres ↔ Pinecone sync layer for a 3.6M-document legal research corpus, keeping vector metadata coherent with the transactional store through a revertible, checkpoint-resumable migration protocol. • Canonicalized ~4,000 messy document-type strings into 47 UI-facing categories against a live 57K-docket production database with zero downtime. • Built a custom NLP summarization model for 10-Q filings at the Federal Reserve Bank, extracting quantitative insights for analyst review. Stack: Python, SQL, PostgreSQL, Snowflake, Azure SQL, AWS, Azure Data Factory, Microsoft Fabric, Power BI, dbt, Airflow, Docker, Git, LangChain, OpenAI API, Anthropic API, Pinecone, pgvector, VoyageAI, RAG pipelines, ETL/ELT pipelines. Background: MS Data Science (Johns Hopkins, 3.8 GPA), formerly Hedge Fund Credit Analyst at JPMorgan Chase.

I help businesses replace manual, repetitive work with reliable AI automation one system I built resolves 2,000+ support tickets per day at 94% auto-resolution, and another eliminated 6+ hours of manual work daily by connecting AI directly to a client's real tools. I don't build fragile AI demos. I started in data engineering and Power BI, so I build AI agents and automation on top of clean data, real integrations, error handling, and reporting your team can actually trust and monitor in production. $450K+ earned on Upwork · 100% Job Success · Top Rated Plus · 371 projects · 3,400+ hours. 𝐖𝐡𝐚𝐭 𝐈 𝐛𝐮𝐢𝐥𝐝 𝐟𝐨𝐫 𝐜𝐥𝐢𝐞𝐧𝐭𝐬: ✓ AI automation workflows in n8n, Make, and Zapier connecting CRMs, email, documents, SaaS tools, and databases ✓ AI agents for support, operations, research, and internal workflows with memory, approval gates, and human-in-the-loop control ✓ RAG chatbots and document intelligence using OpenAI, Claude, LangChain, and vector search ✓ Custom API integrations and MCP servers that expose your existing tools to AI agents ✓ Power BI and Microsoft Fabric dashboards wired to automated, governed data pipelines ✓ Python Bot , Scraping framework with anything possible through Python & framework 𝐖𝐡𝐲 𝐜𝐥𝐢𝐞𝐧𝐭𝐬 𝐡𝐢𝐫𝐞 𝐦𝐞: Most AI automation fails because it isn't connected to clean data, real permissions, audit trails, or proper error handling. I focus on production systems your team can use, monitor, and improve not one-off scripts that break in a week. If you're trying to automate a manual process, connect AI to your existing tools, or turn messy data into reliable decisions, send me a short description of your workflow. I'll map the safest architecture with you before we build anything

With 10 years of strong expertise in Data Engineering, AI/ML Engineering, Big Data Technologies, Python, Cloud Infrastructure, and Business Intelligence, I build end-to-end data platforms and AI-powered systems from ingestion and transformation to analytics-ready warehouses, intelligent automation, and production-grade AI applications. If you need clean, structured, and production-ready data architecture, AI-powered SaaS platforms, or intelligent automation systems, I can design and implement them efficiently. What I Can Help You With AI & Machine Learning Engineering: LLM applications using Claude, OpenAI, and open-source models. RAG pipelines with hybrid retrieval, vector databases, and citation-grounded outputs. Multi-agent orchestration using LangChain, LangGraph, and CrewAI. AI voice agents using VAPI, Retell AI, ElevenLabs, and Whisper. Prompt engineering, structured outputs, and LLM evaluation frameworks. Computer vision, NLP, and predictive modelling. Fine-tuning with LoRA, PEFT, and HuggingFace Transformers. MLOps covering model versioning, experiment tracking, and production monitoring. AI workflow automation using n8n, Make, and custom orchestration. Full-Stack AI Application Development: Production SaaS platforms with AI integration. React, Next.js, TypeScript, and React Native for web and mobile. Python FastAPI, Node.js, NestJS, and Django for backend. Supabase, PostgreSQL, Redis, and Firebase. Stripe subscription billing and multi-tenant architecture. Cloud deployment on AWS, GCP, and Azure with Docker and Kubernetes. CI/CD pipelines and production DevOps. Data Engineering & ETL: Building scalable ETL/ELT pipelines. Apache Airflow and workflow orchestration. Apache Spark, Hadoop ecosystem, and Python. Batch and real-time data processing. Data cleaning, transformation, and quality validation. Workflow automation and scheduled pipelines. dbt for transformation logic and data modelling. Big Data & Streaming: Apache Spark for batch and distributed processing. Kafka, Flink, and Spark Streaming. HDFS and Data Lake architectures. Hive, Pig, and HBase. Distributed data systems. Data Warehousing & Databases: Snowflake, BigQuery, and Amazon Redshift. Star Schema and Snowflake Schema design. OLAP and analytical modelling. Query optimisation and indexing. SQL covering PostgreSQL, MySQL, and SQL Server. NoSQL covering MongoDB, Cassandra, and Redis. Vector databases covering Pinecone, pgvector, Weaviate, and FAISS. Cloud & DevOps: AWS covering S3, EC2, Lambda, RDS, IAM, VPC, ECS, ECR, CloudFront, API Gateway, Bedrock, and SageMaker. GCP covering BigQuery, Dataflow, GKE, Cloud SQL, Cloud Storage, Cloud Run, and Vertex AI. Azure covering Data Factory, Storage, Data Lakes, Azure OpenAI, and Container Apps. Docker, Microservices, and Kubernetes. Terraform for Infrastructure as Code. CI/CD pipelines. Scalable cloud architecture. Cloud Migrations: AWS RDS, Google Cloud SQL, and Azure SQL Database. Moving workloads and applications between cloud providers. Leveraging microservices and containers for cloud migration. Moving data between cloud storage systems, data lakes, and warehouses. Rehosting applications or services with minimal codebase changes. Business Intelligence & Analytics: Power BI and Tableau dashboards. DAX and Power Query. KPI development and metric definition. Business performance reporting. Data storytelling and insights. AI-powered analytics and natural language querying. Data Extraction & Automation: Web scraping using BeautifulSoup, Scrapy, Selenium, and Playwright. Automated data extraction from dynamic and JavaScript-rendered websites. API integration and structured data parsing. CAPTCHA handling and rate-limit management. Scheduled scraping workflows using Airflow and Cron. AI-powered document extraction using OCR, Claude Vision, and Textract. Data cleaning, validation, and structured database storage. Voice AI & Conversational Systems: Production voice AI platforms for businesses. Inbound and outbound AI calling systems. WhatsApp, SMS, and email automation. CRM integration and appointment booking. Multi-language voice agents with real-time conversation. 30 plus AI and software products shipped across healthcare, fintech, e-commerce, legal, and enterprise SaaS. UK-based, remote, and available for long-term engagements.

I’m an ex-Shopify data scientist and analytics engineer with 6+ years of experience, currently working at CoinBase. At Shopify I worked on production-scale analytics systems across the data warehouse ecosystem; ingestion, storage, data modelling, and analytics. I now help businesses design and build data warehouses. I fix storage and ingestion issues, implement data governance, design and orchestrate data modeling, and build analytics workloads for your team. I mostly work in the Google ecosystem (GCS, BigQuery, Looker), but can use whatever platform your business needs (e.g., Databricks, Snowflake, Microsoft Azure, etc.) I have a Databricks certification, as well as have developed hundreds of dashboards across all the different platforms. Why clients hire me: ✅ Deep data platform knowledge (3 years at Shopify) ✅ Clear communication + Loom walkthroughs ✅ Fast turnaround with clean, documented work ✅ End-to-end expertise: tracking, data pipelines, and dashboards If you have a broken pipeline, or need one to be built, please reach out, I'd love to help.

I architect, build, and implement AI systems that turn business data into decisions - conversational analytics, predictive ML models, and autonomous AI agents that automate real workflows. Backed by 16+ years and 400+ delivered projects, I bridge the gap most AI freelancers can't: I don't just prototype models, I design the end-to-end solution and engineer the data foundation, pipelines, and cloud architecture that make AI production-ready, secure, and scalable. I work as a true solution partner - architecting, executing, and implementing around each client's custom business use case, timeline, and budget. From a single AI agent to a full data-and-AI platform, I right-size the solution so you get maximum value without overspending. WHAT I DELIVER • Conversational AI & "Chat with your data" - Ask questions in plain English and get instant answers, trends, and forecasts from your CRM, sales, finance, HR, and ops data. Built on Vertex AI, Gemini Pro, LLMs, and BigQuery ML with retrieval-augmented generation (RAG) over vector databases (pgvector, Vertex AI Vector Search). • Agentic AI & Process Automation — Autonomous, multi-step AI agents that complete tasks, not just chat. I design and ship end-to-end automations with n8n and Make, integrating LLMs directly into your business processes for measurable efficiency gains. • Predictive Analytics & Custom ML — Forecasting, churn prediction, classification, and anomaly detection using BigQuery ML and Python (TensorFlow, PyTorch, scikit-learn, XGBoost). • Solution Architecture & Implementation - End-to-end ownership: discovery, architecture, build, deployment, and handover - tailored to your use case and budget, with scalability, security, and compliance designed in from day one. GEN AI & MACHINE LEARNING • Vertex AI (Gemini Pro / Flash), BigQuery ML, Vertex AI Agent Builder & RAG pipelines • LLM integration, prompt & context engineering, embeddings & semantic search • Vector databases: pgvector (PostgreSQL), Vertex AI Vector Search • Conversational AI, AI chatbots & assistants grounded in your own knowledge • Agentic workflows and AI-driven automation with n8n and Make (Integromat) • Custom ML model training & deployment: TensorFlow, PyTorch, scikit-learn, XGBoost, Pandas, NumPy - for forecasting, recommendation, churn, and anomaly detection • MLOps: model training, evaluation, monitoring, and lifecycle management DATA ENGINEERING & WAREHOUSING (the foundation that makes AI work) • ETL/ELT: Dataflow, Datastream, Dataproc, Cloud Functions (Python/Node.js), dbt, AWS Glue, SSIS, Airbyte, Fivetran • Real-time & batch ingestion: Google/Facebook Ads, GA4, Search Console, Salesforce, REST APIs, JSON/CSV, SQL & NoSQL databases • Warehousing & modeling: BigQuery, Snowflake, Redshift, Azure Synapse, SQL Server, Oracle - star/snowflake schemas, partitioning, clustering, and performance tuning • Databases: PostgreSQL (pgvector), MySQL, MongoDB, MariaDB, Oracle, SQL Server, AWS RDS - deployments, upgrades, and zero-downtime migrations BI & ANALYTICS • Executive-ready, AI-augmented dashboards in Looker Studio, Power BI, Tableau, Looker, QuickSight - KPIs for Marketing, Sales, Finance, HRMS, Operations & CRM CLOUD & GOVERNANCE • Multi-cloud: GCP (primary), AWS, Azure • Secure, scalable architectures: IAM, RBAC, row-level security, VPCs, encryption • Data governance & compliance: access/change management, classification, auditing, GDPR PROVEN TRACK RECORD • 16+ years experience • 400+ successful projects • 50+ data warehouses • 200+ pipelines & dashboards • 30+ zero-downtime migrations • 50+ business-application implementations (CRM, ERP, HRMS, Finance, Supply Chain, SIS/LMS, EHR, Banking, Hospitality) WHY CLIENTS CHOOSE ME I combine a business mindset with deep technical excellence. I architect the right solution for your goals and budget - not the trendiest - and own the full lifecycle from raw data to AI-driven insight and automation. You get solutions that ship on time, within scope, and built to last. Tell me the outcome you're after - a conversational analytics assistant, an AI agent that automates a workflow, or a predictive model - and I will architect the fastest path to value. Skills tags (order matters - AI first) Generative AI · Vertex AI · Gemini · LLM · Conversational AI · RAG · Vector Database · Machine Learning · BigQuery · n8n · Make · Python · TensorFlow · Solution Architecture · Data Engineering

𝗜 𝗱𝗲𝘀𝗶𝗴𝗻 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗱𝗮𝘁𝗮 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝘂𝗻𝗶𝗳𝘆 𝗳𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗶𝗻𝘁𝗼 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. Whether you are battling 12-hour query runtimes, migrating legacy silos, or building an AI-ready data lakehouse, I provide the architectural strategy and engineering rigor required to scale. With 20+ years of experience delivering high-stakes production systems for global brands like Apple, Overstock, and Medline, I move beyond task-based support to own your technical roadmap. As the Managing Director of ADLAB Solutions, I lead a vetted engineering bench capable of scaling delivery across Data Engineering, AI/ML, and UI/UX—ensuring your stack remains a growth driver, not a bottleneck. ──────────────────────────── 📊 THE IMPACT (By the Numbers) • 1B+ Rows modeled cleanly with 99.9% pipeline uptime. • 35% Avg. Reduction in Snowflake/Cloud compute costs through refactoring. • 150+ Dashboards delivered with 70% latency improvement. • 30+ Hours/Week saved per client through metadata-driven automation. ──────────────────────────── 🛠️ ARCHITECTURAL FOCUS AREAS • Warehouse Modernization & Migrations: Specialized in Teradata/SQL Server → Snowflake, BigQuery, or Fabric. I focus on zero-downtime cutovers and "Set-Based" design to maximize cloud-native performance. • Metadata-Driven ELT/ETL Pipelines: Building idempotent, self-healing pipelines (dbt, Airflow, Matillion, ADF). I design systems that handle schema drift automatically to protect downstream AI and BI models. • AI & Agentic Data Foundations: Architecting semantic layers for Snowflake Cortex, RAG-powered knowledge assistants, and predictive "Nowcasting" systems that combine high-frequency signals with broad benchmarks. • Analytics Engineering & BI Governance: Shipping governed Power BI, Tableau, and Sigma environments. I fix the "Broken Trust" in numbers by implementing Row-Level Security (RLS) and centralized metrics definitions. • Real-Time & Managed Services: CDC patterns for sub-minute freshness (Kafka, GoldenGate) and ongoing SLA-based infrastructure monitoring and administration. ──────────────────────────── 🎯 TRUSTED BY Apple • Teradata • Medline • Overstock • Bed Bath & Beyond • Salt Lake Bees • Poppi • Utah Jazz ──────────────────────────── ✅ THE ADLAB ADVANTAGE • Enterprise Governance: Experienced with remote workstations (VDI), M365/Google security protocols, and HIPAA/regulated environments. • Strategic Roadmap: I identify technical debt and map a cost-efficient architecture. • Managed Delivery: My team executes the build with senior-level oversight. 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗮𝗰𝗸 𝗶𝘀 𝗳𝗮𝗶𝗹𝗶𝗻𝗴 (𝘂𝗻𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗿𝗲𝗽𝗼𝗿𝘁𝘀, 𝘀𝘁𝘂𝗰𝗸 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀, 𝗼𝗿 𝗿𝘂𝗻𝗮𝘄𝗮𝘆 𝗰𝗹𝗼𝘂𝗱 𝗰𝗼𝘀𝘁𝘀), 𝘀𝗲𝗻𝗱 𝗺𝗲 𝘁𝗵𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀. 𝗜’𝗹𝗹 𝗽𝗿𝗼𝘃𝗶𝗱𝗲 𝗮 𝗯𝗿𝗶𝗲𝗳 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝘁𝗲𝗹𝗹 𝘆𝗼𝘂 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗵𝗼𝘄 𝘄𝗲’𝗹𝗹 𝗳𝗶𝘅 𝗶𝘁.

Lead data engineer with product analytics and data science background, having worked with Fortune 500 companies (Procter & Gamble, Merck, Anheuser-Busch), as well as top-notch data-driven startups. Skilled in translating complex business problems into data solutions, designing data pipelines, providing high-quality data for data-driven insights and decision-making, as well as building KPIs, conducting statistical analyses, and creating impactful visualizations. Problems I'm good at solving: • Data Warehousing and Analytics • ETL / ELT data pipelines • SQL query tuning • Data Modeling and Database Design • Reporting • Data Analysis • Data Cleaning, Pre-Processing • Data Visualization • NLP problems I have a bachelor's in engineering from the top LATAM university (Universidade de São Paulo) with a track record of supporting organizations across various industries, including remote hiring, real estate, and consumer goods. Skills and Expertise ✅ SQL ✅ Python Databases ✅ Snowflake ✅ Redshift ✅ BigQuery ✅ Athena ✅ Trino ✅ Postgres ✅ MySQL Big Data Cloud Technologies ✅ Amazon Web Services – AWS Certified (Redshift, Athena, S3, Lambda, Glue ...) ✅ Google Cloud Platform Other Data Engineering Tools ✅ dbt ✅ Airflow ✅ Fivetran ✅ Git, Gitlab, and Github ✅ Rundeck ✅ Docker Data Visualization ✅ Looker (LookML Expert) ✅ PowerBI ✅ Metabase ✅ Looker Studio (Data Studio) ✅ Sigma ✅ Hex Data Science and Machine Learning ✅ Sci-kit learn, pandas, etc ✅ NLP analysis ✅ Spark ✅ Databricks ✅ Hex ✅ Jupyter Notebooks User Behavioral Analytics ✅ Snowplow ✅ Indicative ✅ Heap ✅ Amplitude ✅ Google Analytics

Senior Data Engineer building production ETL/ELT pipelines, cloud data warehouses, and analytics platforms that turn fragmented data into reliable, business-ready insight. Over 1,700 hours on Upwork with a 100% Job Success Score, specializing in Airflow orchestration, dbt transformation, and Snowflake/BigQuery warehousing across AWS, GCP, and Azure. 𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 - ETL/ELT pipelines (batch + streaming) - Workflow orchestration with Airflow, Dagster, MWAA, Step Functions - Data ingestion, replication, and CDC with Fivetran, Airbyte, Meltano - Pipeline monitoring, alerting, and cost optimization 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 & 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 - Snowflake, BigQuery, Redshift, Databricks, ClickHouse, Postgres - Star, Snowflake, Kimball, and Data Vault modeling - Schema evolution, partitioning, and clustering - Query performance tuning for BI workloads 𝗖𝗹𝗼𝘂𝗱 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 - AWS (S3, Lambda, Glue, Athena, MWAA, Step Functions) - GCP (BigQuery, Pub/Sub, Cloud Functions, Dataflow) - Azure (Data Factory) - Docker, Kubernetes (EKS/GKE/AKS), Helm 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 & 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 - Apache Kafka, Spark Structured Streaming - Event-driven architectures - CDC pipelines for low-latency analytics 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗕𝗜 - Power BI, Looker, Tableau, Looker Studio - Superset, Metabase, Klipfolio - Semantic layers and self-service KPI dashboards 𝗥𝗲𝗰𝗲𝗻𝘁 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗜𝗺𝗽𝗮𝗰𝘁 - Built a real-time FinTech analytics platform (Markets4U) with ClickHouse and Power BI for sub-second dashboard latency - Designed a smart real estate analytics stack (ECAP) using Airflow, dbt, and Snowflake for automated property reporting - Unified Salesforce and operational data (ASR) into a centralized customer analytics layer with Power BI - Modernized a legacy Excel reporting workflow into a full Airbyte → dbt → BigQuery → Looker Studio pipeline 𝗪𝗵𝘆 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗸𝗲𝗲𝗽 𝗵𝗶𝗿𝗶𝗻𝗴 𝗺𝗲 - 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽: architecture → development → deployment → documentation - 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗳𝗶𝗿𝘀𝘁: data shaped to support decisions, not just processed - 𝗖𝗹𝗲𝗮𝗿 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: frequent updates, no surprises, proactive escalation - 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲: modular, testable, scalable pipelines built to grow with your product Send me a message about your data challenge, I'll review it and recommend the cleanest path forward.

I help companies increase revenue and automate processes with the power of data science and AI. I'm a full-stack data scientist with a wealth of expertise in machine learning, NLP, Generative AI (ChatGPT, LLMs), data engineering, and analytics engineering. I can also help drive strategy and manage your data team. My previous experiences include creating and managing end-to-end data pipelines powering AI & ML products at the WHO and venture-backed startups. I can help you: 💡 Ideate on how to integrate the latest AI & ML techniques (e.g., ChatGPT) into your products and services 🤖 Build ML and NLP models on your custom data 🔧 Set up robust data and analytics pipelines using modern tooling (dbt, Fivetran, Dagster) and software engineering best practices 🏷️ Build in-house data annotation and labeling pipelines to power your custom AI models ☁️ Deploy AI models and manage related cloud infrastructure in a cost-effective manner 🎓 Mentor your existing teams on how to use MLOps and DataOps tooling to supercharge productivity I have a bachelor's in physics and a master's in data science and analytics from Georgia Tech. ________________________________________________________________________________ My toolkit includes: 🧠 Generative AI — LLMs, OpenAI ChatGPT, Gemini, Anthropic Claude, Chatbots, Retrieval Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, PyTorch, TensorFlow, Hugging Face, LangChain, LLaMA, Gemma ⚙️ Data Engineering — dbt, Dagster, Airflow, Prefect, API Integration, ETL/ELT Pipelines, Fivetran, Airbyte, Meltano, dlt, BigQuery, Redshift, Snowflake, DuckDB, Postgres, MySQL, Cube, dbt Semantic Layer 🔬 Data Science & Machine Learning — Natural Language Processing, Classification, Clustering, Regression, Unsupervised Learning, Topic Modeling, Churn Prediction, Jupyter Notebooks, Prodigy, Spacy, Mephisto, Dataset Creation, Scikit-Learn (sklearn), NumPy, SciPy, Polars, Pandas, CuPy 📊 Data Visualization — Tableau, Looker, Omni, Lightdash, QuickSight, Hex, Deepnote, Plotly, Dash, Seaborn, Matplotlib, ggplot, Hvplot, Altair, d3.js ☁️Cloud & DevOps— Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, Docker, CI/CD, Lambda Labs, Coreweave, RunPod, Banana.dev, SkyPilot 💻 Programming Languages — Python, R, SQL, JavaScript, Scala 🤖 Process Automation —Make, Zapier, n8n, Airtable

Analytics Engineer | BigQuery, dbt, Looker Studio — from ad platforms to CRM to internal tools With 3 years of experience as a Business Analyst and Tool Enablement Specialist — including work at TELUS International — I sit at the intersection of two things most freelancers only do one of: building internal tools that teams actually use, and modelling data that leadership can actually trust. Clients come to me when they have one of these problems: — Their team is running on disconnected spreadsheets and manual handoffs — Their ads and CRM data exists but no one can read it clearly — They need a real internal system, not another workaround 🔹 What I build: 1. AppSheet Applications (my core specialty) Task management and operations tools Inventory and IT request tracking systems CRM and workflow apps for non-technical teams Data ingestion pipelines from Google Drive into BigQuery Result: your team works in one structured system they actually adopt 2. Data & Reporting (BigQuery · dbt · Looker Studio) Connect Meta Ads, Google Ads, and CRM data into one place Clean and model your data using dbt and BigQuery Build Looker Studio dashboards with correct metrics — ROAS, CTR, CPC at every level Result: numbers you can act on, not just look at 🔹 Real examples of what I've delivered: ✔️ Built an IT inventory and request tracking app used across departments ✔️ Modelled Pipedrive CRM + Meta Ads data into a unified performance dashboard ✔️ Created a task management system replacing a tangle of spreadsheets ✔️ Built calendar and operations tools connected to BigQuery as the backend 🔹 Tools I work with: AppSheet · BigQuery · dbt · SQL · Looker Studio · Pipedrive · Meta Ads · Google Ads · Google Sheets · Google Drive I combine a deep understanding of how teams actually work with the technical skills to build systems around that. Every solution I deliver is something your team can run independently — not something that breaks the moment I leave. If you want cleaner operations, clearer data, or both — let's talk.