Muhammad Ubaid Ullah Usman's Work | ContraWork by Muhammad Ubaid Ullah Usman
Muhammad Ubaid Ullah Usman

Muhammad Ubaid Ullah Usman

I help startups turn messy data into automated dashboards.

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Lahore, Pakistan

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Lahore, Pakistan

Cover image for This project implements a simple,
This project implements a simple, production-ready AWS data engineering pipeline for analytics reporting. Objective: Build an automated pipeline to ingest data from operational databases and applications, transform it, and deliver dashboards for business users. Workflow: Data is extracted from databases and applications. AWS Glue performs ETL to clean, transform, and structure the data. Transformed data is made available for reporting. Amazon QuickSight builds dashboards for business insights. AWS Step Functions orchestrates the workflow. CloudWatch monitors job execution and failures. IAM manages secure access control. Outcome: A scalable, secure, and automated data pipeline enabling reliable business reporting and decision-making.
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Cover image for Automate Multi-Source Reporting with PySpark & Power BI
Extraction & Cleaning: PySpark notebooks pulled and preprocessed data from APIs and files. Orchestration: Microsoft Fabric Pipelines automated workflows, handled dependencies, and monitored execution. Storage & Transformation: Data loaded into the Fabric Lakehouse and transformed with Dataflows into analytics-ready tables. Visualization: Power BI dashboards provided actionable business insights. Outcome: Fully automated, scalable, and reliable multi-source reporting system.
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Cover image for Built an end-to-end data pipeline
Built an end-to-end data pipeline integrating Salesforce with Snowflake using Fivetran for automated ELT. Designed dbt transformations to create analytics-ready tables and orchestrated the workflow with Airflow. Developed BI dashboards in Tableau/Power BI to provide actionable insights on sales performance, revenue trends, and customer behavior.
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Cover image for Project description. Source: Sybase storing operational
Project description. Source: Sybase storing operational business data. Orchestration: Airflow automated scheduled DAGs for extraction, loading, dependency handling, monitoring, and logging into PostgreSQL. Transformation: dbt applied SQL-based modeling, data cleaning, testing, documentation, and created analytics-ready fact and dimension tables. Visualization: Power BI dashboards built directly on transformed warehouse data. Outcome: Fully automated, scalable, and reliable ELT reporting system enabling faster insights and reduced manual effort.
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