Oliver Cervantes
ES

How I work, told twice.

Analyst by trade, operator by need, tinkerer by habit.⁠1

Operations and analytics at Stripe, after nearly four years in enterprise data at F5. I learn the system, write the query, clean the data and build what keeps the answer current. I use AI along the way and check it against the source. Everything here is told twice: the way a slide would say it, and the way it actually went.

Oliver in a hoodie, smiling, holding up a Talavera tile in one hand and a laptop under the other arm, in front of a tiled wall.
Talavera, the tile Puebla is known for. All pattern, which is how I see data now too. The tiles here are rebuilt from circles and drops, of course.

Three cases, short

ResultIt is in use, and planning by sales segment follows it.

Problem

Targets by seller segment had to reflect past performance and what the business expected next.

Approach

I built a multi-variable model on historical performance and stated business assumptions, with its dashboard, and refined both from stakeholder questions, walking them through scenarios and checking each assumption.

  • Modeling
  • Scenarios
  • Dashboards

Employer work is described in general terms, with no figures or internal details.

  1. The jobThe hands-on part: systems, queries, clean data, things that run.
  2. Track recordWhere I’ve done it, briefly.
  3. Off hoursWalking, music and trees, measured with the same rules.
  4. This siteThe pipelines behind those numbers, and what they may never publish.
  5. LabWhat is half-built right now, and how far along.
  6. Left outWhat isn’t on this page, on purpose.

The job

End-to-end analytics, from raw data to decision-ready dashboards.⁠2

The tools change. The sequence doesn’t. Here it is with one made-up question, from the ask to the job that runs every week.

  1. Step 1

    Ask

    what the question really is: what they asked for, and what they need. Sometimes that is a report they didn’t know to ask for, sometimes no report at all.

  2. Step 2

    Learn the system

    on my own: the tables, the fields, the joins, where each number is born. Then I ask about what still doesn’t add up.

  3. Step 3

    Query and clean

    the data in SQL, and in Python when SQL runs out, with AI to move faster. Nothing gets trusted until I have checked it against the source.

  4. Step 4

    Analyze

    on my own, with AI or without it, until the number is explained and not just reported. When two numbers that should match don’t, I find out which one moved.

  5. Step 5

    Build it to run

    as a dashboard people can check down to the row, or a pipeline that runs without me: scheduled in the warehouse, or a small one on my Mac. Then I write down how it works.⁠3

Tools, roughly in order of how often they settle the questionA conversation · SQL · Salesforce · Snowflake · A spreadsheet · Dashboards · Python (AI-assisted) · Data pipelines · React prototypes · Git · VS Code · Coding agents · AI agents · Scheduled skills

Track record

A track record across operations, analysis and data quality.⁠4

A&C Ops, Data Analyst
On paper

Operations analytics, AI-assisted workflows, and reporting people trust.

In practice
  1. A&C Ops, Data Analyst
    On paper

    Operations analytics, AI-assisted workflows, and reporting people trust.

    In practice

    Apr 2026 — now

    Stripe

    In practiceI learn the systems and build what the numbers run on: dashboards you can check down to the row, a human-reviewed AI workflow for incoming requests, daily SQL pipelines that catch bad CRM data and queue the follow-up so it gets fixed at the source, and a multi-variable target model that is in use for planning by sales segment. AI is in nearly all of it; I decide the question, check what comes back and sign off.⁠5

  2. Business Analyst · Customer Success
    On paper

    Salesforce reporting and dashboards for Customer Success leadership.

    In practice

    Mar 2024 — Jun 2024

    Advantive

    In practiceOn a short contract I built and audited the Salesforce reports and dashboards Customer Success leaders used to track performance, and fixed the ones that were wrong or slow.⁠6

  3. Enterprise Data Management · Data Analyst I → II
    On paper

    Master-data governance, cross-system reconciliation, and migration readiness.

    In practice

    Jun 2022 — Apr 2026

    F5

    In practiceI started on governed account changes, catching duplicates before they were created. I designed the team’s request process and SLA log, which it adopted as standard. I learned SQL by asking to shadow the analysts doing the reporting, which led to GTM reporting and a promotion to Data Analyst II. I finished on migration readiness: reconciling records between systems before they moved.⁠7

  4. Intelligence Intern (internship)
    On paper

    Market-intelligence research, entrepreneurship track.

    In practice

    Feb 2022 — Jun 2022

    Endeavor México

    In practiceIn the internship I segmented companies, kept prospect and survey datasets clean, and analyzed survey responses for entrepreneurship reports: the unglamorous data work behind them.⁠8

  5. Accounting · Data Analyst Apprentice
    On paper

    Financial reporting and business-intelligence development for an accounting practice.

    In practice

    Aug 2018 — now · family firm

    Penagos Mendoza y Asociados

    In practiceAt my family’s accounting firm I maintained operational and accounting data and built basic Excel and Power BI reporting, applying what I was learning in school to real numbers.⁠9 Alongside the day job I maintain the family firm’s website, a structured publishing workflow, and its social-media posts. AI helps with drafts; a person reviews everything before it goes out.

    Appendix D: how Penagos Mendoza is wired →

Background

  • Business Intelligence · UPAEP
  • Business Administration · City University of Seattle
  • Co-author, “Text Mining Applied as Business Intelligence in the Tourism Industry,” De Gruyter, 2022. Read it ↗
  • I am also on LinkedIn ↗⁠10

Off hours

A longitudinal, multi-source personal analytics program.⁠11

I walk. A lot. It is where I do most of my thinking, and trees are most of what I look at. Then, because I can’t help it, I started keeping records of the walking, the music and the trees, with the same rules I use at work. Nobody is waiting on these numbers, so they are a fair test.

Distance
231.7km
Steps per day
11,459per recorded day
+8% vs the year
Marathons
5.5at walking pace
Days recorded
30of 30

The last 30 days, with steps a day set against the year’s pace. The small lines are the last twelve closed months.

  1. Distance
    231.7km
    Steps per day
    11,459per recorded day
    +8% vs the year
    Marathons
    5.5at walking pace
    Days recorded
    30of 30

    The last 30 days, with steps a day set against the year’s pace. The small lines are the last twelve closed months.

    Record 1

    Walking

    231.7 km in the 30 days to Oct 30⁠12

    The ruleTranslate the number into something a person can picture.

    Appendix A: the full walking record →

  2. My eight most played albums since 2013. The bigger the cover, the more plays. The shared account’s albums are left out.

    1. ViewsDrakeNo. 1, 351 plays
    2. ScorpionDrakeNo. 2, 328 plays
    3. In The Lonely HourSam SmithNo. 3, 308 plays
    4. PurposeJustin BieberNo. 4, 276 plays
    5. The HeistMacklemore & Ryan LewisNo. 5, 256 plays
    6. Grandes ÉxitosLuis MiguelNo. 6, 246 plays
    7. HomónimoLola ClubNo. 7, 245 plays
    8. Me RehúsoDanny OceanNo. 8, 240 plays

    Record 2

    Music

    90,112 plays since 2013, once the records were cleaned⁠13

    The ruleA ranking without its sample size is decoration.

    Appendix B: the full listening record →

  3. One mark per living tree. The narrow ones with veins are the five ash trees.

    Record 3

    Trees planted

    10 trees I planted in the last two years that are still alive⁠14

    The exceptionSome numbers are here only because I like keeping them.

Two rules, then: make the number picturable, and show its sample size. They are the job’s habits, applied where nobody is checking.

This site

A privacy-first, automated personal data platform.⁠15

  1. Step 1

    A private source

    Each record starts somewhere I would not publish raw: a step counter, a phone health export, streaming histories, a scrobble log, the site’s own visits.

  2. Step 2

    The Mac mini collects

    Collectors on an always-on Mac mini read the live sources. The two big exports, Apple Health and streaming history, are read once on my Mac and reduced to totals. Raw records never leave the dashed line. An Apple Music export is still on its way.

  3. Step 3

    The validator checks

    Coverage, duplicates and freshness. A run that does not validate changes nothing: the last edition stays up.

  4. Step 4

    Only totals cross

    The gate lets small summaries through into a public file. Nothing finer than a total does.

  5. Step 5

    The page

    The file is committed and deployed, and the live site shows it. No AI subscription is in the loop.

RecordSourcePublishedUpdated
ListeningLast.fm, weekly, and a streaming-history export read on my Mac; Apple Music export pendingTop 20 songs, artists and albums for the month and year, plus monthly totals since 2013; never a single playOct 31, 2026
WalkingFitbit through Google Health, read-only, and an Apple Health export read on my MacSteps and distance for recent windows, plus monthly and yearly totals since 2023; never a single day or routeOct 30, 2026
VisitsVercel Web Analytics, no cookiesVisitors, views, countries, pages and devices, as countsOct 31, 2026
TreesA planting log, kept by handHow many of the trees planted in the last two years are alive, and how many are ashby hand

The pipelines run without me, which is the point. The walking one still needs me now and then; it is on the bench below.

AI coding agents write most of the code; I define the system, its constraints, what gets published and what ships.⁠16 Orchestrating agents is what I am working on most right now: a few agents wired together, and me holding the keys, meaning the repository, the API access and the final yes.

Wondering about the data? It is public too.

See this site’s analytics →

AI-augmented, human-in-the-loop decision intelligence.⁠17

LabOn the bench

A culture of continuous experimentation.⁠18

  • Building
    Decks and prototypes in HTML and React

    Internal decks and small prototypes in HTML and React, and automations I own with engineers, with AI doing much of the typing. What I can show lives on this site.

  • Building
    A walking connection that lasts

    The Google side of the walking record still needs verifying, so it can run without me re-approving it by hand.

  • Later
    Notes

    Short write-ups of things that broke and what fixed them, once there are enough.

Left out

Radical transparency.⁠19

Every bullet point of every job
This page keeps the dates, the roles and how I work. The long version is on LinkedIn.
Employer metrics, systems and partners
Not mine to publish. The descriptions above stay at the level of how I work.
Walking routes, times and places
Distance answers the question I care about. Routes answer a different one: where I live.
Heart rate, sleep and the rest of the health record
The site never receives them.
The full listening history
A ranked selection is honest enough. The rest is just embarrassing.
Where the trees are
They are doing fine without visitors.
Skill bars
No one has ever been 80% good at SQL.
A blog
I don’t have a publishing schedule and won’t pretend to.