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A focused selection of systems and products shaped around real delivery constraints and measurable outcomes.

Selected software engineering, data platform, cloud, and product delivery projects by Muhammad Usman.

Projects

Practical notes on building reliable software, data platforms, and useful AI systems.

Software engineering notes on backend systems, data platforms, cloud delivery, and practical AI work.

Writings

<h2>Case Study: Unified Data Platform (UDP) Big Data Infrastructure</h2><p>An enterprise big data ecosystem engineered for Walmart, abstracting infrastructure complexities via YAML-driven and low-code ETL pipelines running on Apache Beam, Spark, and managed Apache Airflow.</p><h2>The Problem</h2><p>Building enterprise-grade big data pipelines manually forced engineering teams to manage fragmented data infrastructure, complex scheduling systems, and custom database connectors. This lack of standardization slowed down development velocity, increased production vulnerabilities, and obscured execution visibility.</p><h2>Engineering Features &amp; Direct Impact</h2><ul><li><p>Low-Code Ingestion Engine: Integrates diverse database protocols, Kafka data streams, RESTful APIs, and flat-file assets into an abstracted ingestion layer, allowing self-service pipeline creation via declarative YAML workflows.</p></li><li><p>Orchestrated Job Telemetry: Embeds fault-tolerant Notebook and workflow APIs directly into managed Apache Airflow instances, exposing deep, real-time metrics on executing Apache Beam and Spark big data jobs.</p></li><li><p>Automated Validation Framework: Executes automated, end-to-end integration tests for high-volume ETL use cases, significantly cutting down manual validation cycles while maintaining system accuracy.</p></li><li><p>Hardened Security &amp; CI/CD Pipelines: Embeds SonarQube quality gates directly inside automated deployment pipelines, completely eliminating P1 severity bugs and reducing OWASP web vulnerabilities across codebases.</p></li><li><p>Rigorous Coverage Optimization: Drives test suite engineering across multiple backend source repositories, expanding overall test code coverage from an unstable 10% up to an enterprise-grade 80%.</p></li><li><p>Agile Engineering Leadership: Directs cross-functional sprint execution pipelines under strict production SLA metrics, guaranteeing zero operational escalations during critical system upgrades.</p></li></ul><h2>The Bottom Line</h2><ul><li><p>The System: Delivered a centralized, secure data orchestration ecosystem capable of processing massive enterprise data volumes through standardized Spark and Airflow templates.</p></li><li><p>The Business: Shifted engineering teams away from manual data infrastructure management, accelerating time-to-production while raising code coverage to 80%.</p></li></ul><h2>Technical Stack</h2><ul><li><p>Java8</p></li><li><p>Spring Boot</p></li><li><p>ReactJs</p></li><li><p>Apache Kafka</p></li><li><p>Apache Beam</p></li><li><p>Apache Spark</p></li></ul><h2>Link</h2><ul><li><p>Walmart Inhouse Product</p></li></ul>

Case study covering backend platform capabilities, Airflow integration, quality controls, and automated testing.

Unified Data Platform | Project Case Study

Walmart’s centralized enterprise ETL ecosystem for low-code ingestion, Spark and Beam processing, Airflow orchestration, governance, and monitoring.

Unified Data Platform (UDP)

<h2>Case Study: Enterprise Data Landscape Scanner &amp; Lineage Engine</h2><p>A cloud-agnostic metadata management and data lineage framework supporting 40+ enterprise data technologies, automating end-to-end data discovery, transformation tracking, and regulatory governance.</p><h2>The Problem</h2><p>Enterprise organizations lack central visibility into their distributed data assets across fragmented cloud and on-premise ecosystems. This opacity breaks data lineage tracking, increases vulnerability to compliance penalties, and forces reliance on expensive third-party tools to map data dependencies.</p><h2>Engineering Features &amp; Direct Impact</h2><ul><li><p>Serverless Query &amp; ADF Lineage Parser: Extracts pipeline metadata from complex, parameterized Azure Data Factory instances alongside Azure Synapse serverless SQL queries to map dynamic relationships and end-to-end data journeys.</p></li><li><p>Unified Snowflake Cataloging Connector: Integrates streams, security tagging, and relationship cataloging directly into a single Snowflake scanner, eliminating customer reliance on external lineage-tracking utilities.</p></li><li><p>Cloud-Agnostic Databricks Scanner: Designs modular, secure Databricks metadata extractors that maintain cross-cloud compatibility across both AWS and Azure environments.</p></li><li><p>External Lineage Cross-Cataloger: Leads the core integration layer for dbt, Databricks, and Azure Synapse, standardizing distinct metadata schemas into a unified, normalized governance format.</p></li><li><p>Optimized Memory ETL Pipelines: Restructures embedded H2 database query indexes and refactors Groovy/Python scripts, slashing operational memory consumption while accelerating scanner execution speeds by 10% to 20%.</p></li><li><p>System-Wide Vulnerability Hardening: Identifies and remediates critical SQL injection exploits across 25+ distinct data platform scanners to achieve a 99% system protection rate.</p></li><li><p>High-Availability GitOps Migration: Migrates legacy Jenkins pipelines over to automated GitLab CI/CD workflows, cutting developer maintenance overhead while guaranteeing 24/7 scanning infrastructure availability.</p></li></ul><h2>The Bottom Line</h2><ul><li><p>The System: Automated data discovery across a plug-and-play matrix of 40+ modern enterprise technologies, incorporating automated vulnerability patching and optimized stream processing.</p></li><li><p>The Business: Minimized client churn by accelerating product delivery timelines, reducing data management costs, and mapping complete, compliance-ready impact analysis maps.</p></li></ul><h2>Technical Stack</h2><ul><li><p>Java11</p></li><li><p>Akka Stream</p></li><li><p>Apache Kafka</p></li><li><p>Databricks</p></li><li><p>Python and Groovy</p></li></ul><h2>Links</h2><ol><li><p><a href="https://alexsolutions.com" target="_blank" rel="noopener noreferrer">https://alexsolutions.com</a></p></li></ol>

Case study covering metadata discovery, connector security, lineage, and scanner optimization.

Data Landscape Scanner | Project Case Study

A cloud-agnostic metadata discovery and lineage framework supporting more than 40 enterprise data technologies.

Data Landscape Scanner

<h2>Case Study: Building a Zero-Friction B2B Network for Connected Warehouses</h2><p>Architected a multi-tenant platform that lets warehouses sell to dealerships directly through their existing ERPs, featuring zero-friction QuickBooks onboarding and LLM-powered order predictions.</p><h2>Project Information</h2><ul><li><p>Role: Technical Architect • AI Product Engineer • Backend Engineer</p></li><li><p>Industry: Automotive Parts • B2B Commerce • ERP Integration • AI</p></li></ul><h2>The Problem</h2><p>Warehouses struggled to scale sales because selling to external dealerships required manual entry across conflicting software. Dealerships had to browse dozens of isolated portals to find stock, while warehouse admins lacked any predictive tools to forecast costs or order demand.</p><h2>Technical Solutions &amp; Direct Results</h2><ol><li><p>Zero-Friction QuickBooks Onboarding: I built automated background data pipelines that pull legacy products, past orders, and customer profiles instantly upon connection, eliminating manual cleanup for new warehouses.</p></li><li><p>Multi-Tenant ERP Architecture: I designed an isolated database schema that safely hosts multiple ERP networks simultaneously, enabling the platform to scale to new accounting software without risking data leaks.</p></li><li><p>LLM Order Forecasting &amp; Cost Tracking: I developed an AI data pipeline that analyzes historical purchasing habits to predict future dealership stock needs, populating an admin dashboard with automated, itemized cost margins.</p></li><li><p>The Single-Portal Experience: I engineered a real-time synchronization engine for products, prices, discounts, orders, and invoices, allowing dealerships to buy from a wide network of warehouses within a single portal.</p></li></ol><h2>The Bottom Line</h2><ol><li><p>For Warehouses: Sell and ship inventory globally without ever leaving your primary, day-to-day ERP software.</p></li><li><p>For Dealerships: Source from an endless aisle of warehouse inventories through a single, unified screen.</p></li></ol><h2>Technical Stack</h2><ul><li><p>FastAPI</p></li><li><p>PostgresSQL</p></li><li><p>OpenAI</p></li><li><p>Cursor, ClaudeAI &amp; Codex</p></li><li><p>QuickBook</p></li></ul><h2>Links</h2><ol><li><p><a href="https://www.coastlineapa.com" target="_blank" rel="noopener noreferrer">https://www.coastlineapa.com</a></p></li></ol>

Multi-tenant warehouse procurement, ERP synchronization, zero-friction onboarding, and AI-assisted order forecasting.

CAPA Multi-Vendor Warehouse Platform | Case Study

A multi-tenant B2B procurement network connecting US auto dealerships with warehouse inventory and accounting systems.

CAPA Multi-Vendor Warehouse Platform

Engineering scalable products, data platforms, and intelligent automation for ambitious teams.

Selected engineering work

Nine-plus years delivering scalable full-stack products, data platforms, cloud infrastructure, fintech systems, and AI automation.

Senior Full-Stack Engineer and Systems Architect

9+ years building high-throughput systems across Java, Python, React, cloud infrastructure, big data, fintech, and real-time AI for global enterprises and growing products.

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Senior full-stack and systems engineering across Java, Python, React, data platforms, cloud infrastructure, and practical AI automation.

Muhammad Usman

Senior full-stack engineer building scalable products, data platforms, cloud systems, and practical AI automation.