You will get a PII audit: your data anonymization checked before it ships
Rising Talent
Project details
"The PII columns are masked" is not "this file is anonymous." The dangerous data hides where the review never looks: emails inside free-text notes, masks that map one-to-one back to each person, birth-date + postal-code combinations that make 87.5% of rows unique, an erasure request that cleaned one table and missed another. I run your export through a pre-release gate that checks all of that. On the public demo export that a name-based review passed, the gate found 652 hidden PII instances across unlabeled columns, 567 reversible masks and 16 erasure leftovers - and blocked the release. The clean-fixture case proves the other half: a properly anonymized file exits 0, with no false alarms. You get a findings report naming the mechanism behind every hit (not just a count), plus the gate itself so your next export is checked automatically. Honest limits: this is pattern- and statistics-based detection on structured exports, not legal advice and not a substitute for your DPO; the public repo uses synthetic data only; and I never collect, enrich or retain personal data - the gate can run offline on your side if you prefer.
To start: message me a sample - I reply within a day.
To start: message me a sample - I reply within a day.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$115
|
Standard
$225
|
Advanced
$375
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 6 days |
Number of Revisions | 0 | 0 | 0 |
Frequently asked questions
3 reviews
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NK
Natalija K.
Aug 20, 2026
Python / Playwright Developer – Financial Website Data Extraction PoC
Jigon did a very thorough and technically detailed job. I particularly appreciated his transparency about limitations and unexpected findings — he consistently documented issues rather than trying to hide or work around them. The final deliverables were comprehensive, and the technical handover was well structured and useful for continuing the work independently. The project turned out to be significantly more complex than initially expected, and Jigon put a lot of effort into investigating the technical challenges. Overall, I’m happy with the work delivered and appreciate his professionalism and attention to detail.
TF
Tyler F.
Aug 10, 2026
Start for supabase move
Another project completed with Jigon, he is very easy to work with and clear communication. Will continue to work with him and recommend for everyone
TF
Tyler F.
Jul 28, 2026
WNBA Player Prop Model
Highly recommend and will continue doing work with Jigon! Impressed with the details and knowledge he brought. Exceeded expectations!
About Jigon
Data & AI systems you can verify | Evidence, gates, and reports
100%
Job Success
Gyeonggi-do, South Korea - 3:17 am local time
How I work with clients. Fixed price by scope, agreed before anything starts and split into milestones with one named deliverable each, so the cost is settled before the work is done rather than counted after it. Fully async, in writing - no live calls. Handover and tests ship with the work, so nothing depends on me still being around.
What I do:
• Data for automated and physical systems - sensor logs, run telemetry and event streams treated like any other production data: scheduled ingestion, schema validation, drift detection, and failure logging that surfaces a bad run instead of averaging it away. If a machine produced the number, your pipeline should be able to tell you when that number stopped meaning what it used to
• Web scraping and data extraction - messy sources into clean, validated Excel/CSV/JSON, with the rows that need a human flagged instead of silently dropped
• Korean-language data work - native Korean, handled as engineering rather than translation: OCR quality control on Korean documents (spacing around 조사 and 의존명사, character confusion, reading order in tables and vertical text), 자모 NFC/NFD normalization before it quietly breaks your deduplication and accuracy metrics, and structured extraction from Korean sites, forms and records
• Python ETL and pipelines (Postgres/Supabase) - scheduled runs, idempotent loads, and failure logging that actually surfaces failures instead of hiding them
• Document and LLM work - invoice and statement extraction with reconciliation checks, RAG question answering that cites its source and refuses when it can't, plus guardrails and evaluation harnesses that turn "looks fine" into a number
What you get: every deliverable ships with the evidence behind it - what was checked, what failed, and what a human should review. If something can't be done honestly at the scope or price you have in mind, I will say so before we start rather than quietly cut corners.
Ongoing work: most of what I build runs on a schedule, which means it needs someone watching it after launch. I take retainers for monitoring, drift checks and scheduled refreshes on pipelines I built or inherited. Clients usually start with one bounded piece and continue from there - my current client is on his third contract with me.
How I work: fully async, in writing. I use modern AI tools to move faster on drafts and boilerplate, and I personally review, test, and stand behind every deliverable. You get speed and a human who owns the result.
Recent work: a WNBA player prop model (scraper to prediction to live board) and a Supabase database migration for a sports analytics site.
Proof before you hire: my case studies are public, and each one ships with its full source code and the measured numbers attached - what was checked, what it caught, what it missed. Several were built to fail on purpose: I plant known defects in the input, then publish whether the checks caught all of them and how many false alarms they raised, because a checker that never cries wolf and a checker that cries constantly are both useless. Open any project in the portfolio below and the code and the results are linked from it.
Most recent, all public and reproducible: warehouse-quality-gate is a dbt contract on DuckDB - the sabotaged batch loads with zero errors and reports $4,905,051 of revenue instead of $395,751, and the contract fails 12 of 15 tests so the mart is never built. dag-guard reviews Airflow DAGs statically with ast, no Airflow install and no imports - 12 of 12 planted defects caught, including a catchup setting that queues 90,816 backfill runs the moment it deploys. metrics-contract prices definition drift between dashboards instead of just naming it - net revenue 697,691 by the contract, 755,388 on the dashboard, +8.3%. fhir-quality-gate checks FHIR R4 bundles that are already structurally valid - E119 written instead of E11.9 silently drops 3 patients out of a 46-patient quality measure, and nothing errors.
Steps for completing your project
After purchasing the project, send requirements so Jigon can start the project.
Delivery time starts when Jigon receives requirements from you.
Jigon works on your project following the steps below.
Revisions may occur after the delivery date.
Run the release gate on your export
The gate checks unlabeled columns, one-to-one masks, quasi-identifier uniqueness (k) and erasure leftovers - the four ways a masked file is still identifiable.
Deliver findings and the gate
You get a findings report naming the mechanism behind every hit, plus the gate itself so your next export is checked automatically.