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 am a dedicated Data Engineer with a strong foundation in data analytics, specializing in building production-grade data infrastructure, distributed database systems, scalable ETL pipelines, and AI-powered data applications. With 7 years of experience, I help companies: • Transform messy data into automated, reliable, and scalable ETL pipelines for complex financial data that save 100+ hours monthly • Architect distributed database systems: Design multi-tier data architectures for massive-scale record management • Build advanced ETL pipelines with intelligent change detection to process only what's changed, reducing unnecessary processing • Architect cloud-native data solutions and scale data infrastructure across multiple clouds • Automate data workflows to eliminate manual processing and escape Excel Hell • Build Information Retrieval (IR), semantic search, OpenSearch, and Retrieval-Augmented Generation (RAG) systems that make massive datasets, documents, and knowledge bases searchable and actionable • Develop AI-powered data applications, including document intelligence, chatbot, and agent-based solutions integrated with existing business workflows • Modernize legacy data systems with cloud & AI solutions • Optimize data infrastructure to reduce costs and improve performance • Design parallel execution frameworks: Implement isolation patterns enabling concurrent pipeline runs without conflicts • Build data foundations for AI initiatives, including vector search, knowledge retrieval, document processing, and AI automation workflows I have been providing a wide range of services in the realm of data analytics and data engineering such as: • ETL/ELT pipeline orchestration: Prefect, Databricks Workflows & Asset Bundles • Lakehouse & distributed database architecture: Databricks/Delta, YugabyteDB (distributed PostgreSQL), Neo4j, OpenSearch • Custom dbt materializations and incremental models: SCD-2, temporal tables • Database optimization and storage compression • Data validation, quality quarantine & reconciliation frameworks • Per-run schema isolation and parallel pipeline execution • Google Sheets automation and dashboard generation • Excel-to-database migration and formula translation • Multi-source data integration: CSV, Parquet, S3, APIs, databases • Data Cleaning & Transformation at scale • Operational Efficiency Analysis • Financial metrics calculation systems • Multi-cloud architecture design • Infrastructure as Code • Cloud services integration: AWS, GCP, Azure • RAG & LLM data applications: grounded citations, document extraction, NL-to-SQL semantic layers • Automated reporting solutions (Python-Excel integration) • Data Visualization & Dashboarding While the above services encapsulate my core offerings, I am inherently adaptable and thrive on diving into new challenges and expanding my skill set. Seeking great, enthusiastic projects that will provide me with challenging, interesting work that I can learn from and contribute to. My stack: Data Engineering: ✅ Python ✅ SQL (PostgreSQL, MySQL, SQL Server, YugabyteDB, DuckDB) ✅ Prefect ✅ dbt ✅ PySpark ✅ Delta Lake ✅ Databricks Cloud & Infrastructure: ✅ AWS (EC2, S3, Glue, RDS, Lambda, EKS, DynamoDB, ECR, Bedrock) ✅ Google Cloud Platform (BigQuery, GKE, Bigtable, Cloud Functions) ✅ Azure (ADF, Synapse, AKS, Cosmos DB, Azure Functions, ACR, Text Analytics) ✅ Terraform ✅ Docker ✅ Kubernetes ✅ Prometheus Data Storages: ✅ RDBMS (PostgreSQL, MySQL, SQL Server, DuckDB) ✅ Object Storage (S3, Wasabi) ✅ Graph Database (Neo4j) ✅ Key-Value Database (Redis, DynamoDB) ✅ Document Database + Search Engine (OpenSearch) ETL & Data Processing: ✅ Pandas ✅ NumPy ✅ Selenium ✅ BeautifulSoup Spreadsheet Automation: ✅ Google Sheets API (gspread) ✅ Excel automation (openpyxl, xlwings) ✅ Automated dashboard generation Data Visualization: ✅ Matplotlib ✅ Seaborn ✅ Plotly ✅ Power BI ✅ Grafana Backend Development: ✅ FastAPI ✅ Flask ✅ RESTful APIs ✅ GraphQL ✅ Redis ✅ Nginx ✅ Gunicorn ✅ WebSocket AI/LLM & Agent Systems: ✅ OpenAI API ✅ Anthropic API ✅ Gemini API ✅ AWS Bedrock ✅ Agent Development ✅ AI Chatbots ✅ RAG ✅ NL-to-SQL semantic layers ✅ Semantic search

🎖As a Microsoft Partner · Data Architect & Senior Data Engineer · I build Enterprise Data Platforms & Reporting Systems in which every Power BI Dashboard is backed by a well-structured Database · reliable ETL Pipeline · robust Data Modeling & practical Data Analysis 👨🏻‍💻As a Data Analyst & Data Engineering specialist I help companies turn scattered data into trusted reporting systems using: Azure DevOps · Snowflake · Microsoft Power BI · Microsoft Azure SQL · Database · Microsoft SQL Server · Azure Data Factory · Databricks Platform · modern Data Warehousing architecture My core focus is simple: Azure DevOps for reliable delivery Power BI Dashboard systems for executive reporting Database architecture for real scale Snowflake for scalable Data Warehousing ETL Pipeline automation for stable data flow Data Analyst thinking behind every business metric I usually help when data is spread across: SQL Server Microsoft Azure SQL Database PostgreSQL MySQL NoSQL APIs ERP systems Excel files Google Sheets Snowflake Azure Data Factory Azure Data Platform Azure Databricks Databricks Platform dbt data build tool workflows I design the full path from raw data to business reporting: Data source to ETL Pipeline to Database to Data Warehousing to Data Modeling to Data Analysis to Power BI Dashboard to Data Visualization For Azure projects I use Azure DevOps · Azure Data Factory · Git · Terraform · CI/CD · DataOps · structured deployment workflows to make Data Engineering predictable. Azure DevOps is especially useful when your ETL Pipeline needs version control · automated releases · environment separation & stable deployment For Snowflake projects I design Snowflake Database architecture · Snowflake Data Warehousing · Snowflake cost optimization · dbt data build tool workflows · Database layers · Data Integration logic & reporting-ready Data Modeling. A Snowflake system should not just store data. It should make every Dashboard faster · cleaner & easier to trust For Power BI projects I work across the full lifecycle: requirements Database audit ETL Pipeline logic Data Modeling DAX KPI Scorecards KPI Dashboards Dashboard Design Microsoft Power BI reporting Microsoft Power BI Data Visualization Power BI Developer support Power BI Expert consulting Power Bi Report Builder A recent Azure Data Architect project included ADF ingestion workflows + incremental & full-load pipelines + reusable templates + logging + scheduling + error handling + schema drift handling & bronze layer reliability. That is the type of practical Data Engineering foundation I build before dashboards are created I can help with: ▪ Azure DevOps workflows for CI/CD · Git · Terraform · DataOps & controlled data releases ▪ ETL Pipeline and ETL Pipelines architecture with Azure Data Factory · SQL · Python · APIs & dbt ▪ Snowflake Database architecture · Snowflake Developer tasks & Snowflake Data Warehousing ▪ Microsoft Azure SQL Database · Microsoft SQL Server · SQL Database · SQL Server · PostgreSQL ▪ MySQL & NoSQL optimization ▪ Databricks Platform & Azure Databricks workflows for scalable processing ▪ Data Modeling with star schema · DAX · semantic models · KPI logic & Database reconciliation ▪ Microsoft Power BI Dashboard development for finance · healthcare · sales · SaaS · ecommerce & operations ▪ Data Visualization · Dashboard Design · Looker Studio · Tableau Dashboard migration & Excel Dashboard migration ▪ Business Intelligence · KPI Dashboard · KPI Scorecards · Automated Reporting & Reporting Tool setup ▪ Python automation · Python Data Analysis · Data Science · Machine Learning · Statistics Data Analysis · Data Analytics & MLOps-ready reporting If your Database is slow I optimize the structure If your ETL Pipeline is unstable I redesign the workflow If your Snowflake warehouse is expensive I review the architecture If your Power BI Dashboard is beautiful but unreliable I rebuild the Data Modeling layer If your reporting still lives in Excel Dashboard files I migrate it into a scalable Power BI Dashboard & Database setup If your team needs: Database Architect Azure Data Engineer Data Engineer Data Analyst Power BI Developer Snowflake Architect Business Intelligence specialist I can help define the right technical path before development starts I'm not just a Power BI developer. I work as a Data Analyst · Data Engineer & Data Architect who understands how raw data becomes business value I also help teams with: Data Quality Data Governance Data Cleaning SQL Reporting Power BI Reporting Database Documentation KPI logic stakeholder-ready dashboards Send me your current reporting problem & I will show you how I would structure: Azure DevOps workflow Snowflake Database ETL Pipeline Data Modeling Microsoft Azure SQL Database layer Power BI Dashboard ⭐⭐⭐⭐⭐ 🏆Wait, you’ve actually made it to the very end! I highly value your attention. As a reward, I’ll grant you a discount. Just mention "1 in 100" in your first DM

With 15+ years as a Data Engineer and M.S. in Math, my experience includes the development of ETL/Data Warehouse and Analytics dashboards for eCommerce, Marketing, and Banking. 𝙎𝙪𝙘𝙘𝙚𝙨𝙨 𝙎𝙩𝙤𝙧𝙞𝙚𝙨 1. Payments forecast with MAPE < 2%: Banks need to know their monthly cash inflow to plan new credits and attract investments. I have compiled historical payments from existing clients, organized by calendar month, loan date, and credit product. Used this info to iteratively forecast new payments from new credits. 2. Data Quality from 24% to 99%: fixed core table with account balances in a banking DWH. Analytics accounts in accounting equal the sum of the basic accounts. I made a list of clients with analytics and basic accounts that mismatch. Picked one basic account with the most zero balances across all clients. Statistically, it had the most chances of being wrong. Discussed it with the Admin of the source system, and after several find-and-fix iterations, he confessed there is a filter that does not export all the data into the DWH, enforced by security guys. 3. Credit scoring models development, reducing scoring costs by half: A Fintech company was looking for software to build an internal credit score. They were considering SAS, SPSS, and other software. I proposed to them to build a scoring model using the free R-project, and I was hired immediately. Have joined applications and credit histories data and built scoring models using logistic regression. After the AB test with underwriters, the model proved its efficiency and was deployed in production. We stopped paying for credit scores from 3d party provider reducing scoring costs by 2 times. 4. I was optimizing the legal collection process for a bank. The overall collection process starts with contacting the debtor and discussing the payment plan. If the debtor is unavailable or refuses to pay, the legal collection process starts. Lawyers fill in applications for the Magistrates’ court, paying some court fees. The court may decline the application if it reaches the court too late due to the statute of limitations or take the debtor’s side. Even a won case does not guarantee the debtor will pay. He might decide to file for bankruptcy. Given the high number of debts and fees involved in the court applications, legal collection is a costly process. Multiple outcomes make this process manually unpredictable. I was tasked with building a collection scoring model to filter those debts that are worth taking legal actions. I have pulled information on 100K debts that went through the legal collection process past 3 years. My target variable was the sum of payments after legal actions minus legal fees. I have tried many predictor variables, and the most informative were: • debt amount - the higher the debt, the higher the payment amount is expected • days past due - the higher the DPD, the higher the chances of hitting the statute of limitations • debotr’s region - courts' practices vary across the country, with some courts taking debtors’ side more often Once the model has been built, we applied it to the current debts in the legal process and found out that only 30% of debts are worth taking to court. The rest 70% could be sold to collection agencies. The legal collection scoring model helped to understand: • the factors that drive collection profits • automate manual decisions • increase collection profits • clear debts balances, selling them to professional collection agencies 𝙏𝙤𝙤𝙡𝙗𝙤𝙭 • ETL/Data Warehouse: SQL, dbt, Python, Redshift, BigQuery, SAS • Data sources: Shopify, GA4, Google Ads, Meta Ads • Analytics dashboards: Looker Studio, Looker, Power BI, Excel, Google Sheets • Finance math: ROAS, Gross margin, LTV, Cash Recovery • Data Science and ML: Credit Scoring and payments forecasting • Cloud tech: AWS, GCP, Docker • Technical writing: Business Proposal, Product Description, User's Guide, Installation Guide • Project management: Agile / Scrum Let's connect to discuss the details.

⭐⭐⭐⭐⭐ 5.00 across 200+ Jobs AI RAG LLM AGENTIC AI, Vibe coding 🥇𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗼𝗻 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 (𝗗𝗲𝘀𝗸𝘁𝗼𝗽 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁) 🥇𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗼𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 (𝗣𝗟𝟯𝟬𝟬 💎 Top Rated PLUS, Trusted by 210+ clients, 11000 + 🅷🅾🆄🆁🆂 worked, )High-quality outcomes & your trusted companion for the long-term data journey. 12+ Yrs of immense ex 🏅 Top 1% of Tableau Developers 🏅 Top 1% of PowerBI Developers 🏅 Top 1% of Sigma computing Developers Open for a long-term opportunity 15+ years of immense experience in building 200+ solutions and implementing in QlikView Domo, Klipfolio, and Tableau, Power BI projects single-handedly. I also have sound knowledge of ETL, Datamining, data fetching, Oracle database, Google Analytics, Social media analytics. I am also Tableau sales accreditation certified and attended tableau basic and advanced paid training certification as well. I also have snowflake core certification, and also Klipfolio certified expert, please visit my certification section for more info. Skillset: ✅ Tableau ✅ Klipfolio ✅ Qlikview ✅ Domo ✅ Google data studio ✅ Sisense ✅ Looker ✅ Power BI ✅ Click data ✅ AWS Quick sight ✅ Google analytics ✅ Tealium ✅ Airtable ETL Tools: ✅ Azure DataFactory ✅ AWS Glue ✅ Alteryx ✅ Integromat/Make ✅ Knime ✅ Power Automate Databases: ✅ SQL Server ✅ Oracle ✅ Hadoop impala/hive ✅ Mongo DB ✅ Postgres Sql ✅ Snowflake/Amazaon RDS 💎 Top Rated PLUS | 🕐 Fast Turnaround 🌟WHY CHOOSE ME OVER OTHER FREELANCERS? 🌟 ✅ Client Reviews ✅ Communication ✅ Mastery 🟢 GO GREEN 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸🟢 Cloud: Azure (Data Factory, Synapse, Fabric), GCP (BigQuery, Dataflow), AWS Languages: Python, SQL, R, Scala, DAX, JavaScript Orchestration: Airflow, dbt, Prefect, Kafka, CI/CD, Git BI: Power BI, Looker Studio, Tableau, QlikView, Excel/Power Pivot AI/Automation: Clawdbot, Moltbolt, Openclaw, LangChain, n8n, Make, Zapier, Pinecone CERTIFICATIONS 🏅 Tableau Desktop Specialist Certified 🏅 Tealium Specialist Certified 🏅 Microsoft Certified: Power BI Data Analyst 🏅 Google Data Studio Certified 🏅 Alteryx Designer certified 🏅 Microsoft Certified Professional (MCP SQL) 🏅 Excel and Spreadsheets Expert 🏅 Zoho and Looker Expert 🏅 D365 CRM and SharePoint Expert 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗜'𝘃𝗲 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱: - Engineered ETL pipelines processing 50M+ events/day across GCP, Snowflake, and BigQuery - Delivered a $47K enterprise AI + web application rated elite by the client - Replaced manual reporting workflows saving teams 20+ hours per week - Scaled Power BI datasets from thousands to 10M+ rows without performance loss - Built AI document parsing systems handling enterprise-grade extraction and classification - Designed Snowflake data warehouses with optimized dimensional models for executive reporting 𝗣𝗶𝗹𝗹𝗮𝗿 𝟭: 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 ETL/ELT architecture, real-time ingestion, CDC patterns, incremental loads, and warehouse modeling. I work across Snowflake, BigQuery, Databricks, Azure Data Factory, dbt, Airflow, and Kafka. Clean data contracts, reliable refreshes, and systems your team can maintain. 𝗣𝗶𝗹𝗹𝗮𝗿 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 (𝗔𝗟𝗟 𝗧𝗼𝗼𝗹𝘀) Power BI (semantic models, DAX, embedded analytics, Power BI Service, Fabric), Looker Studio, Tableau, QlikView, and Excel/Power Pivot. From KPI frameworks and dimensional modeling to real-time executive dashboards I build reports that are fast, accurate, and aligned to decisions. Performance tuning for slow or bloated reports is a core specialty. 𝗣𝗶𝗹𝗹𝗮𝗿 𝟯: 𝗔𝗜, 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 & 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Production-grade LLM integration using Clawdbot, Moltbolt, Openclaw, LangChain, and RAG architectures. Custom AI agents with guardrails, human-in-the-loop controls, and monitoring for enterprise safety. Workflow automation through n8n, Make, Zapier, Langflow, Flowise, and SimStudio — connecting AI to your CRM, ticketing, email, Slack, and internal systems with role-based access and audit trails. Typical AI deployments: AI support agents, document intelligence pipelines, internal ops copilots, knowledge search with permissions, and intelligent lead qualification systems. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀: Healthcare (EHR, operational analytics, HIPAA-compliant reporting) Finance & Enterprise (P&L, KPI dashboards, multi-source consolidation) SaaS & Startups (product analytics, embedded BI, growth pipelines) 𝗠𝘆 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Every engagement starts with a short audit current-state review, data access, KPI definitions, and a milestone delivery plan with clear timelines. Then we build in iteration cycles with hardening, documentation, and handover so your team owns the system when I'm done. I always leave things better than I found them. Proper data models, clean logic, version-controlled code, and documentation your team can actually work with. Have a project in mind? Click "Invite to Job" let's talk.

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 am a skilled Data Engineer and Analyst with expertise in developing scalable data pipelines, designing efficient data architectures, and creating insightful dashboards. I specialize in data integration, transformation, and analytics, delivering optimized solutions that align with business objectives 🏆 Senior Data Analyst | 9+ Years of Experience 📊 Data Visualization Expert: Power BI, Tableau, Google Looker 🛠️ Data Engineer & Architect: ETL (AWS Glue, Azure Data Factory), SQL (T-SQL, MySQL, PostgreSQL, APIs), Data Warehousing (Snowflake, Redshift, BigQuery) 💻 Data Analysis & Modeling: Python (Pandas, SciPy), R, Predictive Modeling (Customer Churn, Sales Forecasts) Expertise & Skills 🛠️ Data Architecture & Engineering ● SQL Mastery: Skilled in writing optimized queries for extracting and manipulating data across databases (T-SQL, MySQL, PostgreSQL) and APIs. Proficient in joins, subqueries, window functions, and stored procedures. ● ETL & Data Pipelines: Extensive experience in data extraction (web scraping, API integration, database connectors), transformation (data mapping, filtering, aggregation), and loading (bulk inserts, staging tables). Strong background in cloud-based ETL platforms like AWS Glue and Azure Data Factory. ● Workflow Automation: Hands-on experience with workflow management tools like Airflow, Snowflake Tasks and Luigi, managing pipeline scheduling, dependencies, and data flow monitoring. ● Data Warehousing: Deep understanding of best practices in dimensional modeling and normalization. Experienced with Snowflake, Redshift, and BigQuery. 🛠️ Data Cleaning & Wrangling ● SQL-Driven Data Cleaning: Expertise in handling missing values, filtering anomalies, type conversion, and standardization. ● Data Quality & Profiling: Proficient in tools like Pandas Profiling, OpenRefine, and Trifacta Wrangler for data statistics, anomaly detection, and distribution analysis. 🛠️ Data Analysis & Modeling ● Statistical Analysis: Strong background in R and Python libraries (NumPy, Pandas, SciPy, PyTorch) for hypothesis testing, regression, and time series analysis. ● Predictive Modeling: Experience building machine learning models (decision trees, regression) for use cases like customer churn prediction and sales forecasting. ● SQL-Driven Data Exploration: Skilled in uncovering patterns and trends through direct SQL analysis, driving insights for visualization and decision-making. 🛠️ Cloud Platform Expertise: ● AWS: Advanced knowledge of Lambda, Glue, S3, and Redshift for scalable and secure data processing. ● Azure: Extensive experience with Function Apps, Synapse, Data Factory, and Data Lake Gen 2 for robust data solutions. ● GCP: Expertise in BigQuery, Cloud Functions, Data Fusion, and Looker for analytics and data management. ● Snowflake: End-to-end warehouse development including schema design, performance tuning, ELT, RBAC security, semi-structured data (VARIANT), Streams & Tasks, external tables, and cost optimization 🛠️ Data Visualization & Communication ● Data Acquisition & Integration: Experienced in connecting diverse data sources (SQL databases, APIs, CRM systems, Excel/CSV) across Power BI, Tableau, and Looker. ● Data Transformation & Modeling: Strong knowledge of Power Query, Tableau Prep, and LookML for shaping and structuring data effectively. ● Interactive Dashboards & Insights: Adept at building compelling, dynamic dashboards with DAX measures, custom visuals, and row-level security. ● Service Administration: Experienced in managing Power BI/Tableau licenses, configuring user access, and optimizing performance. Skilled in using Power BI API and Tableau Prep for automation. If you're looking for a data-driven approach to problem-solving, let’s connect! I’m happy to discuss projects, provide estimates, or assist with any data-related inquiries.

20-year AI, Data and BI engineer and enterprise strategist who has architected, built, sold, and delivered data solutions , with top individual awards at five consecutive technology giants. ⏤ 𝗖𝗮𝗿𝗲𝗲𝗿 𝗔𝘄𝗮𝗿𝗱𝘀 ⏤ #1 Quota Carrier & Top SE, Fivetran ('23–'24) Best Partner Trainer, Databricks ('25) BD SE of the Year, Qlik Top BD SA, Databricks ('19) Employee of the Year & President's Club, Attunity ('18) Sales Academy Winner & Big Data Black Belt, Oracle IU Young Alumni of the Year ('12) ⏤ 𝐂𝐚𝐫𝐞𝐞𝐫 𝐏𝐚𝐭𝐡 ⏤ ▸ Fivetran / dbt Labs — Sr. Solutions Engineer & ERP CoE Lead. #1 quota carrier two years running. Closed $2.5M annual deal for the 5th largest global hedge fund. Architected cloud migrations for hundreds of Fortune 500 accounts into Snowflake, Databricks, and BigQuery. ▸ Speedboat.pro — Principal Architect & Databricks Training Partner of the Year ('25). 100+ training engagements across ML, data engineering, deep learning, and GenAI for Fortune 100 clients. ▸ Qlik — Principal Solutions Architect. Built SAP Digital Decoupling program generating $40M in referred business. 2x Data+AI Summit speaker. ▸ Databricks — Sr. Solutions Architect. Built CoEs across 86 C&SI partners. Team hit 300% quota Q1 2019, sourced $8.4M in Q4. Onboarded ~10,000 partner consultants. Key wins: T-Mobile, CVS Health, Walmart. ▸ Attunity — Director of Technology Solutions. Co-authored NiFi for Dummies (1M+ copies, $14M in referrals). Employee of the Year 2018 and President's Club. ▸ Oracle — Sr. Sales Consultant, Top 3 ranked SE. Core to billion-dollar EULA closings at GM, Ford, P&G. ▸ Domino's Pizza — BI & DW Architect. Led Netezza-to-Hadoop replatforming; Store Hours analysis returned $10M in savings. ▸ IMC Financial Markets — DBA. Built 50TB SQL Server HPC environment; $280M increase in trading volume. ▸ Halo BI — Chief Product Evangelist. Led first million-dollar deals at a supply chain analytics vendor later acquired for $85M. ⏤ 𝗗𝗮𝘁𝗮𝗠𝗮𝗿𝘁𝘇: 𝗙𝗼𝘂𝗻𝗱𝗲𝗿 & 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁 (2007–𝗣𝗿𝗲𝘀𝗲𝗻𝘁) ⏤ 19-year consulting practice serving IRS, Apple, Chevron, Coca-Cola, and 35+ enterprise clients. Wrote curriculum for Microsoft, Amazon, Google, IBM, Cloudera, and Qlik. 10,000+ professionals trained. Key engagements: ▸ IRS Modernization (Deloitte, Booz Allen, IBM) — Databricks SME Lead: Business Master File modernization, EDP/CADE2 pipeline performance tuning. Active Public Trust/MBI clearance. ▸ Molina Healthcare — Lead Databricks Architect. CDC into Azure Databricks with 60+ dynamic schemas from 12 sources. ▸ Campbells — Lead Databricks Engineer. Metadata-driven SAP connector orchestration framework. ▸ Apple Maps (Pluralsight) — SparkML tuning: hyperparameterization, model selection, custom UDFs. ▸ LiveLine Technologies — VP of Engineering. TensorFlow forecasting with online learning and reinforcement agents deployed to production for manufacturing optimization. ▸ Alivia/UST/Colorado BCBS — Full ETL rewrite and NLP taxonomy extraction from unstructured clinical notes for claims fraud detection. ▸ Capgemini/Coca-Cola — Pod Lead for Databricks CoE. Spark ingestion framework and metadata-driven design patterns. ▸ Anaplan — Sr. Product Manager. Product strategy, presales bootcamp, J&J and Arrow workshops. ▸ Nationwide — Customer Journey CDP strategy, journey mapping, and recommendation engine design. ▸ KPMG/Chevron — Master Data Management on Databricks. ⏤ 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 ⏤ Data Science & AI: Python, Spark, TensorFlow, ML/AutoML, LLMs, GenAI, Deep Learning, NLP Data Engineering: SQL, dbt, Airflow, Fivetran, Kafka, Kinesis, EventHubs, SSIS, ADF, Glue, DataStage, Talend Databases: PostgreSQL, MS SQL Server, Snowflake, Databricks, Oracle, Teradata, Netezza Cloud: ▸ Azure: Data Factory, Synapse, Purview, AI Foundry, Document Intelligence ▸ GCP: Vertex AI, BigQuery ▸ AWS: RDS, Redshift, DMS, Glue BI & Analytics: Tableau, Looker, Power BI, Qlik Engineering: Python, Java, Scala, .NET, Shell, Git, CI/CD, GitHub Copilot, Cursor Platforms: MDM (Profisee, Riversand), ERP/CRM (SAP, Salesforce, Dynamics) ⏤ 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 ⏤ Financial Services Healthcare & Insurance Manufacturing & Supply Chain Retail & Consumer Telecommunications Energy & Oil/Gas Federal Government Technology & SaaS ⏤ 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 ⏤ Chief AI Officer: UChicago Booth ('25) Deep Learning: MIT ('24) AI-Driven Computational Design: MIT ('24) Full Stack MERN: MIT ('22) Industry 4.0: MIT ('21) Cybersecurity: UMich ('21) MS Data Science & Analytics: Michigan State ('13) BS Informatics: Indiana University ('06) Databricks Certified Trainer & Professional Data Scientist: Databricks Microsoft Certified Trainer: Microsoft Google Data Engineering Course Author: Google Oracle Cloud Architect & Big Data Black Belt: Oracle

Senior Data Architect trusted by NASA, the UN, and Mayo Clinic. I design and build production-grade data platforms, real-time streaming pipelines, and scalable analytics solutions. From high-throughput ETL/ELT pipelines to enterprise-scale data lakes, I build secure foundations for modern business intelligence and AI agents with a flawless 100% Job Success rate. If you are looking for a basic SQL scriptwriter, I am not the right fit. I specialize in complex digital transformation, big data architecture, and cost-optimized cloud infrastructure built for enterprise-scale reliability. 🚀 PROVEN CREDIBILITY * Enterprise Portfolio: Trusted to architect mission-critical data ecosystems for NASA, the United Nations, GE, Alstom, Mayo Clinic, Kaiser Permanente, United Health, and Certainti.ai. * Flawless Performance: 100% Job Success Score with consistent 5-star validation from technical stakeholders and data leaders. * AI-Ready Infrastructure: Expert at structuring raw, fragmented data into highly optimized vector data stores and clean pipelines ready for enterprise AI deployment. 📊 ENTERPRISE DATA ENGINEERING SERVICES * End-to-End Data Platforms: Architectural design and execution of data warehouses, modern data lakes, and centralized lakehouses from MVP to production scale. * Robust ETL/ELT Pipelines: Designing automated, resilient, and optimized data movement workflows to eliminate data silos. * Real-Time Data Streaming: Deploying low-latency, real-time data ingestion and processing layers for instant business insights. 🛠️ TECHNICAL CORE & CLOUD ECOSYSTEM * Cloud Data Warehouses: Snowflake, AWS Redshift, Azure Synapse, Microsoft Fabric, OneLake. * Big Data & Streaming: Apache Spark, PySpark, Apache Kafka, Databricks. * BI & Analytics: Power BI, Tableau, advanced data modeling, and robust data governance frameworks. Let’s connect to discuss how we can optimize your data infrastructure for scale, speed, and AI readiness.

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.