esteban@portfolio: ~[email][linkedin][github]
$ whoami

Esteban Cardona

// data & automation engineer

> I architect the data infrastructure and intelligent automations that scale businesses. By engineering unified data systems and leveraging AI, I eliminate operational bottlenecks so distributed teams have the visibility they need and can focus on growth.

cat demos.md
## capabilities

Full-stack across the data lifecycle

[00] ai & llmssecure & private
Embrace AI across your company

Grounded assistants and copilots that help your team do more — wired securely into your company's data, tools and workflows, not the open web. I help teams design, build and ship them end to end.

[01] databases & pipelines
Move data anywhere, reliably

ETL from CRMs, APIs and sheets into a clean, queryable warehouse.

[02] dashboards & analytics
Turn raw data into decisions

Reporting people actually use — the metric, the trend, the action.

[03] web & hosting
Ship the tool, host the tool

Internal tools and web apps, built and deployed end to end.

[04] cloud & automation
Workflows that run themselves

Scheduled, monitored, scalable automations on the cloud.

analytics

A dashboard, wired to the warehouse

One query feeds every view — KPIs, monthly revenue, channel mix and load factor, on a single screen.

Revenue · Dec
$168k
+11.3% mom
Revenue · 12 mo
$1.41M
+104.9% ytd
Q4 vs Q3
+23.6%
quarter on quarter
Avg load factor
82.4%
+11.5% ytd
Revenue & load factor · trailing 12 months
revenueload factor
$200k
$150k
$100k
$50k
$0
100%
95%
90%
85%
80%
75%
87.2%
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Revenue by channel
$1.41M
12-mo
Organic42%
Paid31%
Offline16%
Other11%
Channel performance
Channel
Revenue
Share
MoM
Organic
$591k
42%
+9.4%
Paid
$437k
31%
+14.1%
Offline
$225k
16%
+3.2%
Other
$155k
11%
+21.8%
query.sql
SELECT channel,
       sum(amount) AS revenue,
       round(100.0 * sum(amount)
         / sum(sum(amount)) over (), 1) AS pct
FROM   analytics.bookings
WHERE  ts >= now() - interval '12 months'
GROUP  BY channel
ORDER  BY revenue DESC;
automation

Pipelines that run on their own

Sources land, Python transforms, the warehouse loads — then a dashboard, a Slack ping and AI agents all read from it, nightly and unattended.

spreadsheet
api
crm
databases
etl
serverless · clean · validate
warehouse
queryable store
dashboard
slack_alert
ai_agents
$ serverless deploy
→ packaging service "data-sync" … etl.zip 5.2 MB
→ uploading artifacts → s3 · updating CloudFormation
→ deploying function etl → AWS Lambda (us-east-1) … ✓ 47s
✓ live · schedule rate(1 day) · extract → transform → load → warehouse
#data-alerts
Pipeline BotAPP04:12
✓ Nightly data-sync finished — 1,284,920 rows in 47s
Revenue $168k · +11.3% MoM
Load factor 87.2% · 0 anomalies · all checks passed
## connect_the_dots

any source, any API, any tool — plugged together

Google SheetsBigQuerySnowflakeMongoDBPostgreSQLClickHouseHubSpotSalesforceAWSGoogle CloudStripeTwilioCloudflareSupabaseClaudeChatGPTGeminiGitHub CopilotPythonpandasPlotlydbtFastAPINext.jsReactVercelGitHubTerraformServerlessAnd more

All product names and logos are trademarks of their respective owners, shown to indicate tools I work with — not affiliation or endorsement.

## experience

Seven years, data-driven teams

Data Partner2026 — present
Niko Energy2025 — 2026
Naya Homes2022 — 2025
Nuvocargo2022
CloudKitchens2020 — 2022
Rappi2020
IguanaFix2017 — 2020
## selected_work

Tools I've built

data_enablement_platform
Centralized data infra for Naya Homes, powering team decisions.
payment_link_generator
Self-service web app generating links via Stripe's API.
operational_platform
Permissioned app to fetch, process and display ops data.
automated_review_workflow
Customer reviews consolidated into Slack for accountability.
$ ./lets_build.sh

Have a process worth automating?

Tell me what your team does by hand today. I'll show you what it looks like when it runs itself.

esteban cardona · data & automation engineer · 2026