Software Engineer · Data Analytics · Business Intelligence
I’m a pragmatic software engineer who believes that once you understand how a system works, you can play with it. Right now I’m exploring the full end to end lifecycle, from building web apps and deploying them to the cloud, to collecting, analyzing, and visualizing the data they generate so it becomes clear metrics, BI dashboards, and decisions teams can actually act on.
I
I like the part nobody glamorizes: build the app, deploy it to the cloud, instrument what users do, and turn that data into metrics and dashboards teams can trust.
I don’t separate “software” and “analytics.” A product is a pipeline. Features create behavior, behavior creates data, data becomes models, and models drive decisions. I enjoy connecting those layers end to end, from .NET and Azure to SQL modeling and BI dashboards, so the story stays consistent from database to boardroom.
My Forte : .NET, Azure, SQL, Databricks, Tableau.
I start with the decision, not the chart. First I define the question and KPI like a contract, including edge cases and filters. Then I work backwards through the system, capture the right events, shape the data into a clean model, and finally build a dashboard that explains what changed and why.
II
Three pillars, on purpose. This is not a random pile of skills.
I design analytics ready data models that keep metrics consistent. Clear grain, clean keys, and reliable relationships so reporting stays fast and “the KPI” means the same thing everywhere.
I turn raw data into decision ready insights. I define KPIs with clear logic, validate edge cases, and build dashboards that answer business questions quickly, without chart clutter.
When the data product needs an app layer, I build the plumbing: .NET services, APIs, cloud deployment, and telemetry. If it cannot be measured reliably, it cannot be improved.
III
Proof beats promises. These are structured like case studies, not screenshot dumps.
A performance intelligence dashboard that converts a decade of F1 data into decision-ready driver insights.
Engineered a complete performance evaluation system for F1 drivers using 1950–2024 qualifying and race data. Designed custom KPIs for qualifying strength, reliability, points efficiency, and race-day execution, supported by clean trend visuals that reveal consistency, season momentum, and grid-to-finish behavior.
Problem
F1 performance data is dense, inconsistent across seasons, and hard to compare. Managers need a unified view that answers one question fast: How well is a driver actually performing across qualifying, races, and entire seasons?
Approach
Built a multi-year data model; standardized fields for race results, finish flags, and position deltas; and defined statistical KPIs such as Race Consistency (STDEV-based). Structured the dashboard with a top KPI strip, mid-layer diagnostic charts, and season-level summaries to guide decision-making in seconds.
What it proves
I can translate a real-world competitive domain into measurable performance metrics, model the data for accuracy, and design dashboards that support fast, high-stakes decisions without overwhelming the viewer.
Azure-deployed household inventory app with UPC lookups, health and eco scores, and a single-screen “manage items” flow.
Built a household inventory system where users add items by UPC, enrich them with Nutri-Score and Eco-Score from external APIs, and track quantity, expiry, and storage locations on one page. Deployed to Azure App Service with an Azure SQL backend so it runs as a real, live web app rather than a local demo.
Problem
People track pantry and household items with scattered notes, photos, or memory, and have no easy way to see what they own, where it is stored, or how healthy and sustainable their food choices are.
Approach
Designed a schema-first data model for products, inventory, places, and categories, then built a “Manage Items” page that drives everything. Users enter a UPC, the app calls Open Food Facts and a backup UPC API to pull product details, Nutri-Score, and Eco-Score, and then add quantity, expiry, and storage in-place. The inventory grid, place-wise counts, and success states update immediately without leaving the page.
What it proves
I can ship a small product end to end: model the data, integrate external APIs, build maintainable Razor Pages with validation and clean UX flows, and deploy the full stack to Azure App Service with an Azure SQL Database in a way that is stable enough to share as a live link.
A multi-entity .NET system with schema-first design, clean CRUD flows, and cloud-ready deployment.
Designed the relational schema, implemented multi-level CRUD across related entities, and configured the app for Azure hosting with an Azure SQL backend.
Problem
Manage architecture domain entities with consistent CRUD, stable relationships, and deployable persistence.
Approach
Schema-first modeling, clear entity relationships, validation and error handling, deployment-ready configuration patterns.
What it proves
I can design a database, build the app layer, and ship it like a real system.
Usage patterns, segmentation, and trends turned into a clear, scannable story.
Modeled the dataset for consistent metrics and built a dashboard that highlights behavioral trends and segment differences.
Problem
Explain usage behavior in a way that helps decision-makers spot trends and outliers quickly.
Approach
Defined KPIs, validated filters, and designed the layout for fast scanning with supporting breakdowns.
What it proves
I can turn a dataset into a narrative dashboard with consistent KPI logic.
Pipeline-first thinking: ingest, transform, validate, and shape data for downstream BI.
Built repeatable transformations and quality checks, producing clean outputs suitable for KPI computation and reporting.
Problem
Prepare large datasets for analytics without manual, fragile steps.
Approach
Defined inputs and outputs, applied transformations with validation, and produced consistent schemas for BI use.
What it proves
I understand the system behind BI: pipelines, structure, and quality.
IV
Grouped for scanning. Focused on what I use to build, deploy, model, and measure systems.
Strongest edge: I bridge product engineering and BI, so KPI definitions stay consistent from app events to dashboards.
V
A quick timeline for cross-checking. The real proof is the projects above.
University of Cincinnati · Lead hands-on labs for 120+ students, turning messy business problems into clear, repeatable workflows.
University of Cincinnati · Expected Dec 2026 · Building depth in analytics, data platforms, and applied AI for business decision-making.
HCL Technologies · Built and supported enterprise applications with cross-functional delivery and stakeholder-facing releases.