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Firefly

Data Engineer2026 — now

“Years of scraped Pakistani court judgments sit in messy databases. Make them something a legal product can actually use.”

23steps, run from one config
955judgments in the first full volume
39fields per case

The situation

At Firefly I work on Pakistani case law. The raw material is years of scraped judgments: long blocks of text, inconsistent names, courts that are really section labels, and fields that are simply empty.

The job: turn that into clean, structured data, without ever losing or hiding anything.

What I shipped

  • One pipeline instead of a pile of notebooks. Twenty-three steps, run from a single config file, that pull out judges, parties, outcomes, case types, the laws cited and the courts.
  • A local LLM where rules can't decide. Rules first; a model only where the text genuinely needs reading. Then every value is cleaned into one standard form.
  • Nothing lost or hidden. The raw data is never touched. Every step works on its own copy and writes a before/after report of what it changed, filled or emptied.
  • A hand-check loop. The calls no code can make go to a person once. The answers are saved and applied on every run after that.

Stack

PythonSQLiteLocal LLMs (Ollama)YAML configpytest

Contact

Got an idea?
Let's ship it.

moezkayy@gmail.com ↗ LinkedIn ↗