Ashfaq Ali

Engineered a Streamlit reporting application integrated with SQL and Azure Databricks pipelines, cutting report delivery time by 40%. Automated Python- and Docker-based ETL workflows for scheduled data processing, eliminating ~8 hours of manual effort per week. Built scalable PySpark data transformation pipelines in Azure Databricks for reporting and analytics use cases. Designed and deployed a production LLM-based email-processing pipeline using Claude Opus and the Anthropic API to extract and normalize unstructured email content into structured JSON. Orchestrated data processing workflows using YAML-based pipeline configuration and Azure Databricks/PySpark. Applied validation and data quality checks to ensure reliable pipeline outputs and accurate downstream reporting. Used AI-assisted development tools, including Databricks AI Assistant, to accelerate PySpark development, debugging and solution iteration.

Analyzed large-scale marketing and programmatic advertising datasets using SQL and Python to evaluate campaign performance, audience behavior and targeting effectiveness. Developed reusable SQL and Python transformation workflows to streamline data preparation and reporting. Built automated Power BI reporting solutions to support campaign performance monitoring and decision-making. Conducted exploratory data analysis and prepared datasets supporting marketing mix, attribution and campaign analytics.