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How I Work
DATA · CLARITY · ACTION
I take messy business problems and turn them into clear answers. No fluff, no jargon —
just clean data, honest analysis, and recommendations people can actually act on.
Quick Version
Five years of turning raw numbers into things executives can understand and act on.
I write SQL that doesn't break production, build dashboards people actually open,
and translate between tech teams and business leads without losing anyone.
MS in Business Analytics from Sacred Heart University.
What I Work With
| Skill |
What It Actually Means |
| SQL (PostgreSQL, MySQL) |
I write queries that pull exactly what you need — joins, window functions, CTEs — without slowing down the database or confusing the next person who reads the code. |
| Python (Pandas, NumPy) |
I automate the repetitive stuff. Data cleaning, transformations, analysis that used to take half a day in Excel now runs in under a minute. |
| Power BI & Tableau |
I build dashboards people actually open more than once. Real-time, interactive, and laid out so the answer is obvious — not buried under 14 filters. |
| Financial Modeling |
Excel models that forecast revenue, track budgets, and tell you honestly whether a new initiative will pay off or just look good in a deck. |
| Variance & ROI Analysis |
I figure out why your actuals don't match the plan and whether that investment will actually return anything — or just make someone feel busy. |
| ETL Pipelines |
I move data from messy source systems into clean, usable formats. Think of it as building the plumbing so the kitchen actually works. |
| Business Requirements (BRD/FRD) |
I translate "I need a report" into specs developers can build from. Reduces the back-and-forth by a lot — usually the third build becomes the first. |
| UAT & Process Mapping |
I test systems before they go live and map out workflows to find where things actually get stuck — not just where they're supposed to flow. |
| SAP S/4HANA & ECC 6.0 |
I know where enterprise systems hide their data and how to get to it without breaking live transactions or annoying the IT team. |
| Lean Six Sigma (DMAIC) |
I fix the root cause, not the symptom. Root cause analysis that finds where the problem actually starts, so it stops coming back. |
Where I've Worked
| What I DidOwn 10+ Power BI & Tableau dashboards |
What It MeantReal-time scorecards tracking 15+ KPIs across mortgage programs, cost variance, throughput, and housing market performance across Connecticut. Teams stopped waiting on manual reports. Reporting time dropped 40%. |
| What I DidSQL & Python for month-end close |
What It MeantCTEs, window functions, Pandas, NumPy — pulling from multiple source systems and keeping the data clean throughout. The numbers leadership got were ones they could actually rely on at close. |
| What I DidBuilt Python anomaly detection pipeline |
What It MeantScikit-learn pipeline hooked into the ETL that catches reconciliation exceptions before they reach any report. Cut manual review time by 30% — less time hunting for errors, more time on actual analysis. |
| What I DidAutomated recurring reports with AI |
What It MeantUsed Claude API and GitHub Copilot to automate reports that someone previously wrote manually each week. Cut turnaround time by 25% and freed the team up for work that actually needed a person thinking through it. |
| What I DidTracked performance across 3 business units |
What It MeantBuilt Power BI dashboards covering inventory levels, service rates, throughput, and cost variance so leadership had real-time numbers instead of waiting on a weekly manually compiled report. |
| What I DidVariance analysis and financial modeling |
What It MeantAd hoc financial modeling for supply chain and operations. Supported ROI work for projects under consideration and put together weekly reports for senior leadership that people actually read. |
| What I DidTraced ERP data quality issues |
What It MeantFound where inventory and financial numbers were diverging in the SAP system. Worked with IT and the ERP vendor to fix the root cause — not just patch the numbers downstream in reports. |
| What I DidWrote functional requirements and ran UAT |
What It MeantSpecified what the ERP changes needed to do and tested each one before it went live. Caught two issues in testing that would have caused downstream data problems if they'd shipped. |
| What I DidAnalyzed 3 years of financial and membership data |
What It MeantCleaned the membership database — separated real active members from lapsed, duplicate, and incomplete entries. Then modeled the financial outcomes of three capital expansion options leadership was weighing. Found $12K in annual savings along the way. |
| What I DidBuilt NPV and ROI models in Excel |
What It MeantModeled campus expansion costs, break-even timelines, and returns for each option — including a satellite daycare facility that came out as the strongest financial case. Replaced manual spreadsheet work with structured models tied back to actuals. |
| What I DidBenchmarked 8 regional YMCAs |
What It MeantCompared capacity utilization, revenue per member, and program pricing against peers. Found $25K in revenue opportunities from pricing gaps. The full analysis fed directly into Q4 2024 budget planning. |
| What I DidRan 12 lab sessions for 40+ grad students |
What It MeantHands-on classes in Tableau and Python. Focused on helping students actually explore data and tell a clear story with it — not just run the code and hope for the best. |
| What I DidOne-on-one tutoring, 25+ students |
What It MeantSQL, Tableau, and data cleaning — whatever someone was stuck on, I helped them work through it. 95% satisfaction across 150+ graded assignments. |
| What I DidSupported ERP implementations |
What It MeantGathered requirements for procurement, logistics, and fulfillment systems. Wrote specs detailed enough that the tech team could build what operations actually needed — not what they assumed. |
| What I DidCleaned and analyzed large datasets |
What It MeantSQL and Excel on messy operational data. Found patterns in vendor performance and procurement spend before they turned into expensive surprises at quarter-end. |
| What I DidBuilt logistics dashboards in Tableau |
What It MeantTracked transportation costs, delivery reliability, and service levels. Teams stopped guessing and started making budget calls with real numbers behind them. |
| What I DidApplied Lean Six Sigma tools |
What It MeantUsed DMAIC, 5-Why, and fishbone diagrams to trace why processes kept breaking. Fixed the actual cause — so the same issue stopped coming back three months later. |
Things I Built
Mortgage Program Performance Dashboard & Reporting Automation
View Project →
SQL · Python · Power BI · Scikit-learn · Claude API
Connecticut Housing Finance Authority's finance team was spending 3–4 days every month manually copying data into Excel for leadership reports. I built an automated pipeline that ran overnight, flagged data errors before they reached any report, and gave leadership live dashboards instead of week-old numbers. Reporting time dropped 40%. Manual review time dropped 30%.
Real-Time Operational Dashboard & ERP Data Quality Fix
View Project →
SAP S/4HANA · Power BI · SQL · Excel · UAT
Jabil had 3 business units each tracking their own numbers in separate Excel files. Someone spent a full day every week combining them manually. On top of that, the ERP system had inventory and financial numbers that didn't match between systems. I built the unified live dashboard, traced the ERP discrepancies to their root cause, and fixed them — so leadership finally had one clean, trustworthy set of numbers.
Membership Capacity & Capital Expansion Analysis
View Project →
SQL · Excel · Financial Modeling · NPV · ROI · Benchmarking
Westport YMCA had a major capital decision to make — expand the campus, stay put and improve quality, or open a new daycare. I cleaned 3 years of membership data, built NPV and ROI models for each option, benchmarked against 8 regional organizations, and gave leadership a recommendation backed by numbers. Found $12K in savings and $25K in revenue opportunities along the way. The analysis fed directly into Q4 2024 budget planning.
Education
MS in Business Analytics — GPA: 3.55 / 4.00
Sacred Heart University, Jack Welch College of Business · Fairfield, CT
December 2024 · Predictive Analytics, Data Mining, Database Management, Statistical Modeling, Financial Analytics
BE in Civil Engineering — GPA: 3.46 / 4.00
Sharam Sadhana Bombay Trust, College of Engineering · Jalgaon, India
May 2022