Rakesh T.

𝐈 𝐛𝐮𝐢𝐥𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞, 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐫𝐞𝐚𝐝𝐲 𝐝𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐒𝐐𝐋, 𝐏𝐲𝐒𝐩𝐚𝐫𝐤, 𝐄𝐓𝐋/𝐄𝐋𝐓, 𝐀𝐢𝐫𝐟𝐥𝐨𝐰, 𝐀𝐖𝐒 𝐚𝐧𝐝 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞. If your data is scattered across APIs, databases, CSV/JSON files or business applications, I can help turn it into a clean, reliable and scalable data system. 𝐖𝐡𝐚𝐭 𝐈 𝐛𝐮𝐢𝐥𝐝: • ETL / ELT data pipelines • Python data pipelines • SQL transformations & optimization • PySpark / Spark data processing • Batch & incremental data processing • REST API & database ingestion • Data validation & quality checks • Data warehousing • Snowflake / AWS data solutions • Airflow workflow orchestration • Data modeling & dimensional modeling • PostgreSQL / MySQL data systems • Monitoring, logging & error handling • Production-ready data workflows 𝐌𝐲 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 I don't treat a data pipeline as simply "extract → transform → load." I design for: 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 → 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 → 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 → 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 → 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲. That means validation, retries, logging, incremental processing, idempotency, failure handling and clear documentation where the project requires them. 𝐓𝐲𝐩𝐢𝐜𝐚𝐥 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 APIs / Databases / Files ↓ Python / PySpark ↓ Validation & Transformation ↓ Airflow / Orchestration ↓ Snowflake / AWS / PostgreSQL ↓ Analytics / BI / Business Applications 𝐂𝐨𝐫𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐬𝐭𝐚𝐜𝐤 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: Python, SQL 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Pandas, PySpark, Apache Spark 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: ETL, ELT, Data Pipelines, Data Integration 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: Apache Airflow 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠: Snowflake, PostgreSQL, MySQL 𝐂𝐥𝐨𝐮𝐝: AWS 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Git, GitHub, Docker, REST APIs I’m a strong fit for projects involving: • New data pipelines • Legacy pipeline modernization • API-to-warehouse ingestion • ETL/ELT development • Data warehouse implementation • SQL and pipeline optimization • Python/PySpark processing • Data quality and reliability • Batch processing and automation