Data Engineer (Python / Web Scraping / Economic Data)

We’re looking for a hands-on Python data engineer to own our data pipelines end-to-end. Full time, month-to-month.

You’ll pull data from public economic/financial sources, build web scrapers for digital signals, clean messy real-world datasets, and ship automated scripts/APIs that feed our models. You’ll collaborate with our modeling team, but you’ll own the design and implementation of your pipelines.

If you like scraping, wrangling weird time-series data, and turning it into clean, reliable inputs, this will be fun.

What You’ll Do
+ Collect data from public economic/financial sources (World Bank, FRED, gov APIs, etc.)
+ Build and maintain web scrapers for search data and other online signals
+ Clean and standardize messy, real-world datasets for ML/time-series models
+ Automate data pipelines (scheduled scripts / cron / simple workflow tools)
+ Build simple APIs (e.g., FastAPI/Flask) to serve processed data to our modeling team
+ Monitor data quality, handle edge cases, and fix things when sources change
+ Document your data sources, transformations, and APIs

Must have skills
+ Python (pandas, numpy; BeautifulSoup/Scrapy/Selenium for scraping)
+ Experience building data pipelines: cleaning, transforming, feature engineering for time-series/economic data
+ Experience with API integration + simple APIs (FastAPI or Flask)
+ Comfortable with basic ML concepts (how clean data feeds models; features, splits, leakage)
+ Experience automating scripts on a schedule (cron / workflow tools)

Nice to have:
+ Geospatial libraries (GeoPandas, Shapely)
+ Track record conducting economic/financial data engineering
+ Computer vision or image preprocessing experience
+ Cloud (AWS/GCP) for simple deployment / storage
+ Hands-on experience with PyTorch or TensorFlow