CFA Level I Candidate

Blake Sanchez
Aspiring Financial Analyst

Economics graduate · Data-driven builder · Las Vegas, NV

I turn raw data into decisions. By day I'm pursuing the CFA charter; by night I'm building automated analytics systems that monitor, measure, and report — because good analysis should run itself.

REAL DATA — HOME NETWORK SPEED, LAST 10 HOURLY TESTS · AS OF OCT 2, 2026, 6:00 AM PDT
Capabilities

Skills

A working toolkit, not a wishlist. Everything listed here is something I use in production on my own systems — or am actively building toward professional fluency.

Excel Building

PivotTables, INDEX/MATCH and XLOOKUP, conditional logic, data cleaning workflows. Currently working through structured daily practice toward financial modeling fluency.

SQL Building

SELECT, JOINs, aggregations, subqueries, window functions. Practicing against real datasets with a focus on the query patterns financial analysts use daily.

Financial Modeling Core

DCF fundamentals, three-statement mechanics, ratio analysis, and valuation multiples — grounded in an economics degree and CFA Level I curriculum.

Data Visualization Applied

Chart.js dashboards, KPI design, time-series charts. I build dashboards that answer one question fast — no chart junk, no vanity metrics.

Python (Automation) Applied

Scripting ETL-style pipelines, API integrations (Plaid, Telegram), scheduled jobs, log parsing. Every project below runs on Python I wrote and maintain.

Home Lab / Systems Applied

Mac mini M4 server: network-wide DNS ad blocking (AdGuard Home), automated backups with offsite pull, uptime monitoring, cron orchestration, Tailscale networking.

Selected Work

Projects

Financial models and operating systems built to turn messy business questions into decisions, with transparent assumptions and working outputs.

📡

Home Network Analytics Dashboard

PYTHON · CHART.JS · OOKLA · CRON

A self-updating operations dashboard for my home network — the kind of monitoring stack an SRE team would recognize, built for one household.

  • Hourly automated speed tests with Telegram alerts on 50%+ drops vs. 24-hour average
  • Bandwidth tracking every 15 minutes, aggregated into daily usage trends
  • Network device discovery and mapping across the LAN
  • Disk-space monitoring with threshold alerts
1,155
Mbps baseline
24/7
Monitoring
15min
Refresh cadence
💰

Automated Finance Briefing System

PYTHON · PLAID API · TELEGRAM API · CRON

A personal FP&A pipeline: pulls live banking data every morning and delivers a plain-English financial brief to my phone before markets open.

  • Daily Plaid sync across checking, brokerage, and retirement accounts
  • Tracked net worth calculation with day-over-day deltas
  • Spending anomaly detection — flags unusual transactions automatically
  • Weekly spending summaries every Sunday evening
Daily
Brief cadence
6:00am
Delivery time
Auto
Anomaly flags
🛻

Tundra Maintenance Tracker

PYTHON · TELEGRAM API · JSON STATE · CRON

Total cost-of-ownership tracking for my Toyota Tundra — maintenance scheduling, fuel economy analysis, and service history in one automated system.

  • Odometer-based maintenance scheduling (5,000-mile oil intervals, filters, rotations)
  • Fuel economy trending across 25+ tanks (~15.8 MPG on 35" tires)
  • Telegram reminders for upcoming service with mileage countdowns
  • Reply-to-log interface: text the odometer, the system records it
25+
Tanks tracked
15.8
Avg MPG
5k
Mile oil interval
📊

ClarityDash — CONCEPT

PRODUCT DESIGN · DASHBOARD UX · SMB ANALYTICS

A business-dashboard product concept for small businesses that can't afford a data team — plug in their tools, get the five numbers that matter.

  • Designed KPI-first dashboard layouts for service businesses
  • Scoped data-source integrations (POS, accounting, scheduling tools)
  • Focused on the insight gap: SMBs drown in software, starve for answers
5
KPIs per dashboard
0
Analysts required
Featured case study · Financial modeling

The True Cost of a Late Invoice

What does it actually cost a typical Las Vegas plumbing or HVAC shop when customers pay 30, 60, or 90 days late?

Every 30 days customers pay late costs a $500K shop about $4,100 a year — roughly 8% of its profit — just to finance its own receivables.
At 60 days late, the annual financing cost reaches $8,200: about one-sixth of the year's profit, gone to carrying customers.

01The model

A shop doing $500,000 in annual revenue at a 10% net margin earns $50,000 a year. With Net-30 terms, it already carries about $41,096 in average receivables. Each additional 30 days of lateness adds another month of revenue to the balance the shop must finance.

ScenarioDSOAverage receivablesExtra working capitalAnnual financing costShare of profit
Paid on time30 days$41,096$0$00%
30 days late60 days$82,192$41,096$4,1108.2%
60 days late90 days$123,288$82,192$8,21916.4%
90 days late120 days$164,384$123,288$12,32924.7%

Receivables = annual revenue × days sales outstanding ÷ 365. Financing cost = extra working capital × 10% APR.

02How the cash hole opens

The shortfall arrives one billing month at a time. Under 90-day-late payment behavior, the shop is carrying $125,000 of customer cash flow by month three. The balance remains tied up until collection speed improves.

CUMULATIVE CASH SHORTFALL VS. PAID-ON-TIME · 12 MONTHS
Cumulative cash shortfall by lateness scenario Thirty days late remains at 41,667 dollars across all twelve months. Sixty days late rises from 41,667 dollars in month one to 83,333 dollars from month two onward. Ninety days late rises from 41,667 dollars in month one to 83,333 dollars in month two and 125,000 dollars from month three onward. $0 $50K $100K $125K M1 M3 M6 M9 M12 30 days late 60 days late 90 days late

Assumes even monthly billings of $41,667. Each plateau is the steady-state cash drag while invoices remain late.

03The hidden second cost: chasing

Four hours a week × 52 weeks × $50 per hour = $10,400 a year of owner time spent following up on overdue invoices. Combined with the $8,219 financing cost, a shop paid 60 days late is out approximately $18,600 a year, or 37% of annual profit.

04Assumptions, stated plainly

05Built for analysis and sales

This case study also serves as sales collateral for PaidUp, my invoice-chasing software for trade businesses. It turns a vague operational frustration into a decision-ready estimate that an owner can challenge, customize, and use.

Download the working Excel model
Edit the blue input cells for revenue, margin, payment terms, APR, owner rate, and chasing time. The scenario table and monthly cash-shortfall chart recalculate from live formulas.
Download .xlsx

06Sources

Background

About

I'm Blake Sanchez, an economics graduate from UNLV pursuing a career as a financial analyst. I'm currently a CFA Level I candidate, working through the curriculum while building the technical toolkit the role demands.

My approach to learning is to build real systems. Instead of just studying Excel formulas, I automated my personal finances. Instead of reading about monitoring, I built a network operations dashboard. Every skill I claim has a running system behind it.

I'm looking for an entry-level financial analyst or data analyst role where I can combine economic reasoning with hands-on data skills — and keep building from there.

Now
CFA Level I Candidate
Working through the CFA curriculum: ethics, quantitative methods, financial reporting, and valuation.
Now
Technical Skill Building
Daily structured practice in Excel and SQL, targeted at financial analyst workflows.
Ongoing
Home Lab & Automation
Self-hosted analytics infrastructure: monitoring, alerting, ETL pipelines, and reporting — all automated.
UNLV
B.A. Economics
Foundation in micro/macro theory, econometrics, and quantitative reasoning.
Get in touch

Let's talk

I'm actively pursuing financial analyst and data analyst opportunities in the Las Vegas area and remote. If you're hiring — or just want to talk data — I'd like to hear from you.