BACKGROUND
As a software engineering and product management intern on the Experience and Data Team, I helped lead the migration of Azure Cloud Data from legacy SQL Cubes to a modern data pipeline using Azure Synapse Analytics, improving performance, reliability, and access. I also designed interactive Power BI dashboards to aid analysis for cross-functional teams.
PROBLEM
Legacy SQL Cubes were slow, hard to scale, and difficult to use, which caused friction. Teams were finding it difficult to engage with cloud usage data.
OUTCOME
I migrated the data to a modern data pipeline using Azure Synapse Analytics, and designed interactive Power BI dashboards for analysis. This allowed teams to understand Azure cloud data faster and more clearly, helping teams be more agile.
GOALS
USER GOAL
As a data analyst, I want to easily understand the dataset features and quickly extract insights.
BUSINESS GOAL
We want to make analyzing Azure cloud data cheaper and more efficient for analysts.
PROBLEM STATEMENTS
As Microsoft scales its cloud infrastructure, the legacy SQL Cube based system was outdated and created friction. How can we bridge our Data processing with graphical insights to gain improvements with real business workflows?
SQL CUBE LIMITATIONS
KEY INSIGHTS
SQL Cube dimensions grew exponentially, and with years of tech debt, analysis became slow and complex.
SQL Cube Dimensions grow exponentially as there are more dimensions
PROPOSED SOLUTION
To solve this problem, I used Azure Synapse Analytics as a modernized Extract, Load, and Transform (ETL) tool to analyze Azure Cloud Data. The benefits we get from this architecture include
Using modern, scalable technology supported by enterprise
Seamless integration with Power BI Analytics Tools
Ability to converge data between multiple sources to create a unified workspace
Simplified architecture of Power BI integration
MY ROLE
As a hybrid software engineer and product manager, I worked across various layers, migrating data systems while designing visualization tools for analysts to use. Acting as a mediator between data engineers and product managers, I was able to translate needs and increase my team's OKRs by driving improvements in data engagement and decision making efficiency. To accomplish this project, I took the following steps:
IMPACT
IMPROVED DATA INSIGHTS
As data systems scale, technical debt and latency compound fast. By replacing outdated cube-based infrastructure with a scalable Azure Synapse pipeline, this work reduced friction in how internal teams access and interpret data. This ensures faster refreshes, fewer failures, and a smoother path from raw telemetry to business-critical insights.
REFLECTIONS & TAKEAWAYS
Working deeply with data pipelines helped me understand how to think about dimensions and structure data. Working as a product manager and an engineer also helped me bridge the product tech gap and understand how to be a product forward developer.
✨ Huge thanks to my team and mentors for guiding me through this project!
