Project Portfolio - Disha Awtani

  Company Background Global Credit Corp operates global financial and analytical workloads that depend on accurate, up-to-date currency exchange rates for reporting, reconciliation, and downstream analytics. With operations spanning multiple regions and currencies, manual handling of exchange rates introduced risks such as incorrect reporting, inconsistent rate usage, and delayed analytics. Project Overview An end-to-end automated currency exchange ingestion pipeline was implemented using Microsoft Fabric. The solution integrates external REST API ingestion, structured warehouse storage, JSON parsing using T-SQL, and orchestrated, scheduled pipelines. Fresh exchange rates are captured daily, stored in a normalized warehouse schema, and made readily available for analytics and reporting. Project Objectives Automate daily ingestion of currency exchange rates from an external API. Eliminate manual updates and ad-hoc processes. Store exchange rates in a query-optimized warehouse table....

  Company Background Global Retail Inc. is a multi-regional retail organization operating across diverse product categories and customer segments. The company depends on transactional sales data to track regional performance, identify top-selling products, and monitor revenue trends over time. Project Overview An end-to-end, automated, and scalable analytics platform was designed using a modern Lakehouse architecture. The solution transformed raw Azure SQL data and supporting files into a Power BI dashboard, integrating incremental ingestion, Bronze–Silver–Gold transformations, star schema modeling, and orchestrated refresh workflows to ensure reliable and up-to-date analytics. Problem Statement Analytical refreshes were manual and disconnected from ingestion pipelines. Data freshness was uncertain, reducing trust in reports. There was no automated dependency management across ingestion, transformation, and reporting layers. Business users lacked flexibility to analyze sales by reg...

  Company Background GreenGrid Solutions is an energy service provider piloting a smart electricity billing model for residential customers. The company is shifting from flat-rate billing to dynamic, peak-hour pricing to reduce grid strain and promote efficient energy consumption. Project Overview The pilot faced operational challenges due to inconsistent meter readings, manual billing logic, and data privacy risks. Raw meter data contained invalid values, pricing calculations lacked standardization, and customer email IDs were exposed in downstream datasets. The project delivered an end-to-end data engineering solution using Microsoft Fabric, covering ingestion, cleansing, enrichment, privacy protection, and billing calculations within a Lakehouse architecture. Problem Statement Meter data included invalid and unrealistic kWh values. Billing calculations were manual and error-prone. Customer email IDs were exposed in analytical datasets. There was no separation between valid ...

  Company Background Innovate Solutions Inc. is a rapidly growing global technology consulting firm with multiple international delivery centers. Rapid expansion over the past two years led to a significant increase in workforce size, creating challenges for HR and Finance teams in tracking employee data, compensation, and hiring trends effectively. Project Overview The project focused on building a foundational workforce analytics engine using PySpark to enable data-driven decision-making for HR, Finance, and leadership teams. Fragmented HR data was transformed into actionable insights covering compensation, hiring patterns, departmental distribution, and employee tenure. Problem Statement As the workforce expanded, HR lacked clear visibility into hiring trends and employee tenure. Finance struggled to monitor salary distribution and identify high-cost employees. Leadership teams were unable to answer basic workforce questions without manual intervention, resulting in delayed...

  Company Background TelcoPrime is a large telecommunications provider serving millions of customers across multiple regions. As part of its Customer 360 initiative, the organization aimed to unify customer data to support marketing personalization, churn analytics, and operational decision-making. Historically, customer data was ingested directly into the Bronze layer without validation, resulting in fragmented and unreliable datasets. Current Situation Customer records in the Bronze layer suffer from major data quality issues. Key attributes such as address, state, and phone number are inconsistent or invalid. This lack of cleansing at ingestion caused downstream issues, including inaccurate marketing campaigns and unreliable data science models. The Bronze layer, though intended as raw storage, became an unfit analytics source. Key Challenges Customer addresses appear in multiple inconsistent formats, reducing standardization and direct mail effectiveness. State values vary...

  Company Background The City of Metropolis launched a city-wide Lakehouse initiative to centralize data from departments such as Police, Transportation, and 311 Services. A shared cloud-based Central Data Inbox was created as a single entry point for incoming files. Due to the lack of automation and standards, the inbox became congested, impacting analytics efficiency and data governance. Current Situation All departments delivered files to the same inbox. Analysts manually reviewed files and triggered pipelines, which led to duplicate processing, skipped files, unsupported formats being ignored, and no audit trail. As data volumes increased, analytics and reporting slowed. Key Challenges No automated file detection or classification, heavy reliance on manual processes, duplicate ingestion due to missing cleanup, unsupported formats not tracked, poor auditability, and inconsistent downstream data availability. Objective Design a single automated, metadata-driven ingestion fram...