Sports Data Engineer / Historical Sports Data Curation
Budget: $250 – $750 USD
Sports Data Engineer — Historical Sports Data Collection & Validation
ISO Sports Intelligence is seeking a data engineer or sports data specialist to build clean, structured historical datasets for validation and backtesting.
The work is focused on data quality, not sports predictions.
Phase 1 (Paid Sample)
Deliver one historical MLB slate consisting of 5–10 games from the same historical date.
Each game must be provided in a one-row-per-game format.
Required fields include (where available):
* Date
* Home Team
* Away Team
* Opening Moneyline
* Closing Moneyline
* Opening Run Line
* Closing Run Line
* Opening Total
* Closing Total
* Starting Pitchers
* Confirmed/Probable Lineups
* Weather
* Ballpark/Roof Status
* Bullpen Usage
* Key Injury Information
* Final Score
* Data Source
* Missing Data Flags
The dataset must represent only information that would have been available before first pitch, with the final score included separately for post-game grading.
Important
* Accuracy is more important than speed.
* All sources should be documented.
* Missing information should be flagged rather than guessed.
* Experience with sports data, spreadsheet organization, Python, APIs, or data engineering is preferred.
This project begins with a paid sample. Larger MLB and NFL historical datasets will be awarded only after the sample passes validation.
ISO Sports Intelligence is seeking a data engineer or sports data specialist to build clean, structured historical datasets for validation and backtesting.
The work is focused on data quality, not sports predictions.
Phase 1 (Paid Sample)
Deliver one historical MLB slate consisting of 5–10 games from the same historical date.
Each game must be provided in a one-row-per-game format.
Required fields include (where available):
* Date
* Home Team
* Away Team
* Opening Moneyline
* Closing Moneyline
* Opening Run Line
* Closing Run Line
* Opening Total
* Closing Total
* Starting Pitchers
* Confirmed/Probable Lineups
* Weather
* Ballpark/Roof Status
* Bullpen Usage
* Key Injury Information
* Final Score
* Data Source
* Missing Data Flags
The dataset must represent only information that would have been available before first pitch, with the final score included separately for post-game grading.
Important
* Accuracy is more important than speed.
* All sources should be documented.
* Missing information should be flagged rather than guessed.
* Experience with sports data, spreadsheet organization, Python, APIs, or data engineering is preferred.
This project begins with a paid sample. Larger MLB and NFL historical datasets will be awarded only after the sample passes validation.