Hasaan W.

𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 | 𝗘𝗧𝗟 | 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 | 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 | 𝗣𝘆𝘁𝗵𝗼𝗻 | 𝗦𝗤𝗟 I’m an Azure Data Engineer focused on building reliable data pipelines, ETL workflows, data transformation solutions, and analytics-ready datasets using Azure Data Factory, Azure Data Lake, Databricks, PySpark, Python, SQL, and Power BI. I’ve worked on practical data engineering projects involving 800K+ records, 180K+ supply-chain records, CSV-to-Parquet conversion, Azure Data Lake Gen2, Azure SQL Database, automated ETL workflows, and Power BI reporting. My approach is simple → take messy or high-volume data, build a clean and reliable pipeline around it, and turn it into structured data that businesses can actually use. ━━━━━━━━━━━━━━━━━━━━ ▸ 𝗪𝗛𝗔𝗧 𝗜 𝗕𝗨𝗜𝗟𝗗 ━━━━━━━━━━━━━━━━━━━━ ◆ Azure ETL & Data Pipelines → Build end-to-end ETL pipelines using Azure Data Factory → Extract data from CSV, Data Lake, and other sources → Transform, clean, filter, and structure large datasets → Load processed data into Azure SQL and other destinations ◆ Databricks & PySpark Solutions → Develop scalable data transformations with PySpark → Process large datasets efficiently → Perform data cleansing, filtering, aggregation, and analysis → Build Data Lake → Databricks → Azure SQL workflows ◆ Data Lake & File Processing → Azure Data Lake Gen2 data processing → CSV → Parquet conversion → Structured and analytics-ready datasets → Data preparation for reporting and business intelligence ◆ Data Analytics & Reporting → Prepare clean datasets for Power BI → Perform exploratory data analysis with Python/Pandas → Build reporting-ready data models → Convert raw operational data into meaningful insights ━━━━━━━━━━━━━━━━━━━━ ▸ 𝗖𝗢𝗥𝗘 𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞 ━━━━━━━━━━━━━━━━━━━━ ✔ Azure Data Factory ✔ Azure Data Lake Storage Gen2 ✔ Azure Databricks ✔ PySpark ✔ Python ✔ SQL ✔ Azure SQL Database ✔ Pandas ✔ ETL / ELT ✔ Data Cleaning & Transformation ✔ Data Pipelines ✔ CSV & Parquet ✔ Power BI ✔ Data Analysis ✔ Data Integration ━━━━━━━━━━━━━━━━━━━━ ▸ 𝗜𝗡𝗗𝗨𝗦𝗧𝗥𝗜𝗘𝗦 𝗦𝗘𝗥𝗩𝗘𝗗 ━━━━━━━━━━━━━━━━━━━━ ◆ Supply Chain & Logistics ◆ Business Intelligence & Analytics ◆ Data & Technology ◆ Operations & Reporting ◆ Enterprise Data Management ◆ Business Data Processing ━━━━━━━━━━━━━━━━━━━━ ▸ 𝗪𝗛𝗬 𝗖𝗟𝗜𝗘𝗡𝗧𝗦 𝗖𝗛𝗢𝗢𝗦𝗘 𝗠𝗘 ━━━━━━━━━━━━━━━━━━━━ ✔ Practical Azure experience → I work with real data-engineering workflows rather than only theoretical concepts. ✔ Large dataset handling → Projects include processing 800K+ records and analyzing datasets containing 180K+ records and 40+ columns. ✔ End-to-end thinking → From raw CSV/Data Lake data → transformation → database → reporting. ✔ Clean & structured pipelines → I focus on making data easier to maintain, analyze, and reuse. ✔ Business-focused results → The goal isn’t just to move data. It’s to create reliable datasets that support reporting, analysis, and better decisions. ✔ Clear communication → I keep technical work understandable and provide straightforward progress updates. ━━━━━━━━━━━━━━━━━━━━ ▸ 𝗥𝗘𝗖𝗘𝗡𝗧 𝗪𝗜𝗡𝗦 ━━━━━━━━━━━━━━━━━━━━ ◆ 800K+ Record Data Pipeline → Built a pipeline to extract 800,000+ CSV records using Azure Data Factory → Converted the raw data into Parquet format → Prepared the processed dataset for analytics → Created a Power BI report from the resulting data ◆ Azure Data Cleaning Pipeline → Extracted data from Azure Data Lake Gen2 → Performed data cleaning and transformation → Loaded the processed data into SQL Database → Created a complete Data Lake → ETL → SQL workflow ◆ 180K+ Record Supply Chain Analysis → Worked with 180,000+ supply-chain records and 40+ columns → Used Python and Pandas for data filtering and analysis → Extracted useful patterns and insights from the dataset ◆ Databricks Data Lake Integration → Extracted data from Azure Data Lake → Processed and transformed it using Python/PySpark in Azure Databricks → Transferred the resulting data into Azure SQL Database If you need someone who can turn raw data into clean, structured, analytics-ready information, I can help. → Whether you need a new Azure ETL pipeline, Databricks/PySpark transformation, Data Lake integration, SQL data processing, or Power BI-ready dataset, I’m ready to build a practical solution around your requirements. Let’s discuss your data workflow and turn it into a reliable, scalable pipeline. ━━━━━━━━━━━━━━━━━━━━ ▸ 𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦 ━━━━━━━━━━━━━━━━━━━━ Azure Data Engineer, Azure Data Factory, Azure Databricks, Azure Data Lake, Azure Data Lake Gen2, PySpark, Python, SQL, Azure SQL Database, ETL, ELT, Data Engineering, Data Pipelines, Data Integration, Data Transformation, Data Cleaning, Data Processing, CSV to Parquet, Parquet, Pandas, Power BI, Business Intelligence, Supply Chain Data, Data Analytics, Big Data, Cloud Data Engineering