Zachary W.

I turn messy, manual data into clean pipelines and dashboards people use: data analytics, data visualization, data scraping and data extraction in Python, SQL, Power BI and Tableau. M.S. in Data Analytics, 6+ years of data engineering in industry. If you're copying numbers between spreadsheets, pulling the same report by hand every week, or sitting on data locked inside PDFs and websites, that's the work I do. 👇 🔍 𝗪𝗵𝗮𝘁 𝗜 𝗱𝗼 • 𝗗𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: exploratory analysis, regression, forecasting and KPI reporting • 𝗗𝗮𝘁𝗮 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Power BI, Tableau and SSRS dashboards, plus geolocation maps • 𝗗𝗮𝘁𝗮 𝘀𝗰𝗿𝗮𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝗲𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: PDFs, documents and web sources turned into structured tables • 𝗗𝗮𝘁𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗮𝗻𝗱 𝗘𝗧𝗟: Python and SQL pipelines, Pentaho, flat files into real databases • 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗜: predictive sales and demand models, time series, clustering and NLP 🏭 𝗪𝗵𝗲𝗿𝗲 𝗜'𝘃𝗲 𝗱𝗼𝗻𝗲 𝗶𝘁 • 𝗔𝘃𝗶𝗮𝘁𝗶𝗼𝗻: replaced a manual aircraft engine valuation process with automated Python and SQL pipelines, and scraped part data out of PDFs into a maintained related-part matrix • 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗴𝗼𝗼𝗱𝘀: built purchase-to-stock matching that reconciles sales against on-hand and vendor inventory, plus predictive restock models, for Liquid Death • 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲: built and maintained Pentaho ETL and SQL pipelines at Northwestern Mutual • 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴: moved ERP data into SAP and Power BI at MOOG, including interactive ArcGIS KPI maps for demand planning • 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: built a patient care database and Tableau GPS dashboards, and maintained 80+ NY DSRIP quality care measures 💡 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁 Most data projects don't fail at the analysis, they fail at the handoff. I write the extraction and the pipeline so it runs on a schedule without me, and I hand over scripts you can read. You get the dashboard and the thing that keeps feeding it. ✅ 🧰 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝘁𝗲𝗰𝗵 ▪️ Languages: Python (Pandas, NumPy, Scikit-Learn, TensorFlow, Keras), SQL, R (tidyverse, ggplot2, Shiny), SAS, JavaScript (D3) ▪️ Data and ETL: MySQL, SQL Server (SSMS), MongoDB, Pentaho, Spark (PySpark), Hadoop ▪️ Visualization: Power BI, Tableau, SSRS, ArcGIS, Excel ▪️ Cloud and systems: AWS, Azure, Git/GitHub, Jupyter, SAP (Fiori, Ariba), Salesforce ▪️ Notebooks: Jupyter, Google Colab, R Markdown ▪️ Business systems: SAP (Fiori/Ariba), DevOps, Jira, ServiceNow, Microsoft Office 🧠 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 Machine learning and statistics: regression, forecasting and time series, random forests, clustering, neural networks and LSTM, Monte Carlo simulation, NLP and geolocation analysis 📩 𝗟𝗲𝘁'𝘀 𝘁𝗮𝗹𝗸 Send me a sample file, the site or document you need pulled apart, or a screenshot of the report you're building by hand. I'll tell you what's realistic and what it takes before you spend anything.