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MIGUEL RODRIGUEZ / DATA, CODE & DESIGN

I turn messy datainto useful tools.

Senior data analyst. Automation builder.
A designer who can also build the tool.

Follow one invoice View résumé

Alteryx billing workflow~9 hours → 20 minutes/month

Alteryx · Python · Power BI · React / TypeScript
Reported 2023 result · initial setup separate

01 / BILLING AUTOMATION

Follow the record.
Find the difference.

DEMO-104 · From a vendor export to a reviewable $160 mismatch.

01 / Vendor export.xlsx
RecordVendorBilled
DEMO-102 Juniper LLC$1,080
DEMO-103ALDER-co$1,200
DEMO-104NORTH_CO $2,140
DEMO-105 Juniper LLC$1,440

Different names. Different date formats.

02 / Map the fields
NORTH_CO North CoJan-26 2026-01DEMO-104 stays with the row.

A consistent schema for the next file.

03 / Match the records
key = vendor + period
    + invoice_id

join(export, ledger, key)
delta = billed - expected

Simplified logic · source stays attached.

04 / The exception
DEMO-104
Billed$2,140
Expected$1,980
Difference+$160

Flag the difference for analyst review.

05 / Review coverage
Hover, focus or tap a point
0 checked12 checked

9 matches. 3 items that need a decision.

06 / Review queue
104 Amount mismatch107 Duplicate key109 Missing account

The three exceptions, with their sources.

LOCKTON · BILLING AUTOMATION

One monthly task.
Hours given back.

The exception is visible. The monthly rebuild is gone.

Monthly processing timeLower is better
≈96%

less monthly
processing time

THE HIGH-LEVEL IMPACT

8 hours 40 minutes back per monthly run.

Clean the inputs, consolidate invoices, surface discrepancies—and put the recovered time into client decisions.

Reported in my Alteryx Day presentation, 2023. One workflow example; initial setup is separate. All invoice cards use fictional sample data.

Scroll to follow the record
Skip story
NEW / APPLIED AI & SYSTEMS ENGINEERING

FAULTLINE

A healthy service can still be wrong.

Explore real local failures, compare a runbook with a local model, and follow every record through the repair.

Enter the interactive lab Read the engineering case study
CAPTURED INCIDENTOne state. Three repairs.
RestartLegacy behaviorRunbookExplicit rulesLocal modelBounded actions

Each outcome is checked against the original business events.

Inspect the branches
01 / THE BILLING WORKFLOW

Find the exception.
Give the time back.

Every month, the same spreadsheet cleanup stood between an analyst and the real work. I built an Alteryx workflow that consolidated vendor data and put discrepancies in view.

MY PART

Workflow design, vendor and month controls, data preparation, reconciliation, and analyst enablement.

See the build and reported result
MONTH-END / JANUARY 2026

Follow the invoice.

Fictional sample
NORTH_CO Jan-26 · DEMO-104North Co2026-01 · DEMO-104+$160DEMO-104 · Needs review
12records imported
9matched
3need review
The same month arrives three ways. Keep the original row so every change can be traced.
InvoiceVendorSource periodBilledFile
NORTH_CO Jan-26$960vendor.xlsx
Juniper LLCJAN 2026$1,080export.csv
ALDER-co2026/01$1,200billing.xlsx
NORTH_CO Jan-26$2,140vendor.xlsx
Juniper LLCJAN 2026$1,440export.csv
ALDER-co2026/01$1,560billing.xlsx
NORTH_CO Jan-26$1,680vendor.xlsx
Juniper LLCJAN 2026$1,800export.csv
ALDER-co2026/01$1,920billing.xlsx
NORTH_CO Jan-26$2,040vendor.xlsx
NORTH_CO JAN 2026$1,680export.csv
ALDER-co2026/01$2,280billing.xlsx
DEMO-104 / Amount mismatch

The difference is the next action.

Billed$2,140Expected$1,980Difference+$160

The billed amount is $160 above the ledger. Check the vendor rate before approving the invoice.

See the transformation logic 
vendor = title_case(trim(replace_separators(raw.vendor)))
period = parse_month(raw.period)
key = (vendor, period, raw.invoice_id)

expected = ledger.lookup(key)
delta = raw.billed - expected.amount
review_if(duplicate(key) or delta != 0 or missing(account))

Original simplified example. Alteryx powered the reported workflow; Power Query is also part of my data-preparation practice.

12 fictional rows demonstrate the process. The reported ~9 hours → 20 minutes/month comes from my separate 2023 Alteryx workflow.

02 / EMPLOYEE SENTIMENT INTELLIGENCE

A rating is a number.
A comparison gives it meaning.

A score alone cannot tell a team where to focus. I connected research, source-specific parsing, Power BI comparisons, and reporting so the context traveled with the numbers.

MY PART

Python research tools, metric definitions, dashboard design, product offerings, and executive presentations.

Follow the ESA product story
ESA / RESEARCH RATINGS

A score with context.

Fictional companies
FOCUS COMPANYCedar Works
Source: research samplePeriod: Q1 2026Unit: rating, 1–5

How the sample rating changed

24 months · 1–5 scale
12345Apr 2024Apr 2025Mar 2026
Hover, focus or tap a point
01 / PRESERVE THE SOURCE
Cedar Works / Career Opportunities
Captured rating
3.2 / 5
Source reference
research-sample / cedar / Q1-2026
Missing distribution
Unavailable · not inferred from a mean
Select a dimension
12345
Cedar WorksSelected peer average
Career Opportunities

A gap worth investigating.

-0.60 points vs selected peers
FROM CHECKED VALUES TO AN EDITABLE BRIEF

Career Opportunities

Cedar Works3.2 / 5Selected peer average3.80 / 5Gap-0.60 pts
Values are calculated from the sample. The observation remains a draft for analyst review.
Inspect the ratings and calculation
CompanyCareer OpportunitiesIncluded
Cedar Works3.2 / 5Focus
Juniper3.9 / 5Selected peer
Alder3.7 / 5Selected peer
Maple3.8 / 5Selected peer
peer_average = mean(selected_peers.same_scale_ratings)
gap = company_rating - peer_average
report.chart_values = checked_values
report.observation = draft_for_analyst_review

The peer average is an unweighted mean of the selected company ratings. It is not an industry benchmark.

Original illustrative reconstruction of the earlier research-rating workflow. The newer Voice Lab reporting app uses a different percentage scale and remains in development.

03 / AI ORCHESTRATION

Follow the work
behind the answer.

More agents can mean more unsupported claims. I build systems where research, critique and review have different jobs—and every finding can be traced back to a source.

MY PART

Product interfaces, Python workflow contracts, role-based orchestration, evidence ledgers, and review states.

Explore TradeLab research Explore the local AI platform
RESEARCH / ILLUSTRATIVE REPLAY

Follow the claim.

Human review
“Did the new guide improve onboarding?”
FROM SOURCE TO SOMETHING USEFULExplore a layer
One connected system.

Data + code + design

Checked values

Validate the calculation before it becomes a claim. A useful answer separates what is known from what still needs review.

WORKING HYPOTHESIS

The guide improved onboarding.

The initial claim is plausible, but it needs testing against the source material.

CHECK THIS BEFORE CONCLUDINGCompletion time before and after launch.
E-01 / SOURCE DETAIL

The release adds an onboarding guide.

06 May · Fictional research evidence
See how a finding keeps its source 
{
  "claim": "Guide adoption in sample: 60%",
  "evidence": ["E-02"],
  "calculation": "18 / 30",
  "review": "supported",
  "unknown": "effect on completion time"
}

A fictional research example informed by TradeLab and local orchestration work. It runs entirely in this page.

What I’ve built.

Go deeper into the problem, the decisions, and the work behind each result.

Browse all case studies
Applied AI · systems engineering · Completed

FAULTLINE

A real PostgreSQL failure lab: break an order workflow, compare conventional and local model repairs, and verify every resulting record.

Data operations · Completed

Billing & workflow automation

A recurring spreadsheet task became a configurable Alteryx workflow, with the unresolved invoices kept in view.

Analytics product · Completed

Employee Sentiment Intelligence

Research tools, comparable ratings and executive reporting brought employee experience into a repeatable analytical product.

AI research system · Ongoing

TradeLab

A research workbench that connects hypotheses, source evidence, critique and simulated decisions.

Infrastructure system · Ongoing

Local AI Orchestration

A local AI platform that gives planning, focused execution and independent review explicit jobs and visible receipts.

Local business website · Prototype

Dino’s Subs

A heritage-led, multi-route redesign that turns a dense single-page restaurant site into a focused, mobile-friendly journey for menu discovery, party subs, story, gear, careers, and visits.

Desktop product · Ongoing

_Blackbox

Recorded work becomes a searchable history, with timestamps that take you back to the moment behind an answer.

Web and creative systems · Ongoing

TDC · web, media & review

Bilingual web and media work, plus a contextual review tool that lets a stakeholder point to the exact part of a page.

There’s a creative side
to the system.

Interactive rooms, motion, media tools and identity studies. The same care goes into how something feels as how it works.

Explore the interactive gallery
An original illustrated room from the Room Alive project

Bring me the problem.
Let’s build the next step.

Analytics, automation, interfaces and media—built around the people using them.

Data & visualization

Turn complex information into a clear story and a useful next step.

  • Executive dashboards
  • Research & insight
  • Visual storytelling
Patterns in the feedbackIllustrative data
0255075100ABCDEF
Hover, focus or tap a point
Discuss a project Related work

Power BI / Python / Alteryx / Executive reporting

The tools behind the work

Analytics

Decision‑ready reporting from messy, multi‑source evidence.

Power BI / Power Query / DAX / Excel / Alteryx / Executive dashboards

Data & AI

Practical intelligence with traceable inputs and human review.

Python / Sentiment analysis / NLP / AI‑assisted analytics / Structured data products

Systems

Bounded automation designed for evidence, safety, and replay.

Agent orchestration / Local AI / Workflow automation / Typed interfaces / Evidence boundaries

Product

Useful experiences shaped around real people and decisions.

Stakeholder discovery / Executive storytelling / UI/UX / Productization / Team enablement

Professional portrait of Miguel Rodriguez

I turn complex evidence into analytics products, automation, and agentic systems that help people make clearer decisions and move faster.

Built privacy‑aware executive reporting with industry benchmarking across a large proprietary evidence base.

English & Spanish · Native / bilingual

From operations
to building the system.

Miguel Rodriguez

Senior Data & AI Systems Builder

Dallas–Fort Worth, Texas

Lockton

2018—2026
2025—2026

Senior Data Analyst

May 2025 — April 2026 · Plano, Texas · Hybrid

Owned the architecture, automation, and quality of data products that connected research evidence to executive and growth decisions.

  • Advanced a national Employee Sentiment Analysis capability and its delivery model across business divisions.
  • Designed AI‑assisted analytical workflows with clear review, privacy, and evidence boundaries.
  • Enabled a broader analyst team while retaining system architecture and quality ownership.

Data products / AI‑assisted analytics / Power BI / Python

2023—2025

Data Research Analyst

August 2023 — May 2025 · Dallas, Texas · Hybrid

Productized sentiment research and built a configurable toolchain spanning collection, analysis, reporting, and narrative delivery.

  • Created Full, Comparative, and Benchmark ESA offerings for repeatable delivery.
  • Built configurable Python research utilities and packaged them for nontechnical teammates.
  • Connected intake, automated collection, Power BI, presentation output, and reviewed AI‑assisted narratives.

Sentiment analysis / NLP / Python / Automation

2022—2023

HRO Data Analyst

May 2022 — August 2023 · Dallas, Texas · Remote

Developed privacy‑aware dashboards and research views that made complex workforce and market evidence usable by leaders.

  • Built executive Power BI reporting across thousands of proprietary data points.
  • Added industry benchmarking and audience-appropriate privacy controls.
  • Translated emerging analytical needs into reusable reporting patterns.

Power BI / DAX / Benchmarking / Executive reporting

2020—2022

Benefits Analyst

March 2020 — May 2022 · Dallas, Texas · On-site

Delivered benefits analysis and operational reporting while embedded in a complex client environment.

Concurrent contractor assignment supporting MoneyGram

  • Improved reconciliation and reporting workflows with Power Query and structured analysis.
  • Balanced stakeholder service with careful handling of sensitive benefits data.

Benefits analytics / Power Query / Reconciliation

2018—2020

Benefits Specialist

November 2018 — March 2020 · Dallas–Fort Worth

Built the operational and domain foundation that later informed analytics automation and product development.

  • Supported benefits operations, reporting, and stakeholder communication.
  • Identified repeatable work suited to structured data and automation.

Benefits operations / Reporting / Stakeholder support

Earlier experience
June 2017–March 2018

Auditor

United Way of Metropolitan Dallas

Contract audit and data-quality support.

February 2015–May 2017

National Sales Data Analyst

Hilton Worldwide

Revenue reporting and frontend/functionality contributions to an internal lead-automation system.

March–November 2014

Workforce Analyst & A/R Billing

CSC ServiceWorks

Reduced a recurring billing and data‑merging workflow from roughly three days to about five hours.

Bartender

La Duni

Hospitality experience that keeps the person on the other side of the experience in view.

Read the full résumé Download PDF

Building a data
or AI team?

I bring analytical depth, hands-on implementation and the design skills to make the result usable.

Open to US‑remote and DFW hybrid/on‑site opportunities

HireMe@miguel.systemsDiscuss a role View résumé