Case Studies & Examples

Below are examples of the types of automation and data engineering outcomes I deliver.

🔧 Excel Reporting → Automated Pipeline

Before:
Team spent 8–12 hours weekly merging CSVs and updating spreadsheets manually.

After:
Python + SQL pipeline refreshes data daily and updates dashboards automatically.
Result: Saved 30–50 hours per month.

🔧 Broken SQL Pipeline → Clean, Scalable ETL

Before:
Frequent failures, inconsistent tables, unreliable analytics.

After:
Rebuilt ETL in Azure + Databricks, added monitoring and alerting.
Result: 99% pipeline reliability and faster insights.

🔧 Cloud Cost Optimization

Before:
Azure workloads running inefficiently and overspending.

After:
Optimized clusters, queries, and storage layers.
Result: 20–40% savings.

🔧 Internal API for Operations

Before:
Employees manually downloaded and cleaned data from multiple third-party systems.

After:
FastAPI service fetches, merges, and cleans data automatically.
Result: Eliminated 100+ manual tasks each month.

🔧 FastAPI Microservice for Automated Data Integration

Before:
A client relied on manually downloading CSV exports from three different SaaS platforms (CRM, billing, and support tools). Staff spent 1–2 hours daily downloading files, normalizing columns, merging datasets, and emailing spreadsheets to leadership teams. This created delays, inconsistent data, and human error.

Challenges:

Data arrived in different formats
Manual workflows caused reporting delays
No centralized API to fetch, clean, or validate data
No automated delivery mechanism for stakeholders

After:
I built a production-ready FastAPI microservice that automated all data retrieval, cleaning, merging, and publishing steps.

Key Features Delivered

Automated API ingestion from all third-party systems
Data normalization filters and schema mapping
Automated cleaning & validation using Pydantic models
Integrated SQL write-back for analytics & BI dashboards
Scheduled batch processing via background tasks
Authentication and role-based access controls
Logging + monitoring delivered through structured logs

Result

Eliminated 100+ manual tasks per month
Reduced delivery time from hours to seconds
Provided a single, stable API endpoint for all integrated datasets
Improved data consistency and reduced operational overhead
Enabled real-time dashboard refreshes using the API as a data source

Tech Stack

FastAPI • Python • Pydantic • SQLAlchemy • Azure SQL • GitHub Actions • Docker • OAuth2 Auth