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PDF data extraction and Supabase backend

Hiring evidence involving Python, FastAPI, PostgreSQL. First observed 28 Aug 2026; the response opens on upwork.com.

Budget
Not provided by source
Work format
Hiring
Observed
28 Aug 202654 days old
Deadline status
Deadline not provided — verify source
Response destination
upwork.com

Source job details

Structured facts are published only when supplied by the source; missing values are never invented.

Hiring organization
Undisclosed US client
Source reference
OPP-20260829-158
Location evidence
USA; worldwide
Employment type
contractor
Required skills
Python, FastAPI, PDF parsing, LLM extraction, Supabase, PostgreSQL, RLS, provenance, entity resolution
Expected duration
3–6 months; >30 hours/week

Opportunity summary

PDF data extraction and Supabase backend. The source classifies this as hiring work involving Python, FastAPI, PostgreSQL. Location or remote eligibility: Worldwide; needs three hires and shows one hire. Named specialist required..

Technology and scope

The reported stack is Python, FastAPI, PDF parsing, LLM extraction, Supabase, PostgreSQL, RLS, provenance, entity resolution. Check the source for required versions, deliverables and which technologies are mandatory.

Engagement details

Hiring: an employment-oriented search for an individual contributor or team member. Source timing: 3–6 months; >30 hours/week

Budget

Budget not provided by source. Confirm the rate or project value before preparing a proposal; no estimate has been substituted.

Dates and availability

First observed by Leadiy on 28 Aug 2026, 21:01 UTC. Public since 7 Sept 2026, 00:30 UTC. Deadline not provided — verify source. A listed deadline does not confirm that applications are still accepted. These are observation and publication dates, not a reconstruction of the source posting date. Date-only deadlines have no supplied timezone; the date changes here at midnight UTC.

Related market context

In the current 30-day Hiring Pulse, Python appears in 196 of 622 observed opportunities (31.5%). Counts are multi-label, so one opportunity may contribute to several technology buckets. This is a separate aggregate sample, not a confirmation of this opportunity’s availability.

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