We build internal products and automation for our own teams to improve day-to-day business processes. Most tasks revolve around processing large datasets (millions of rows) and turning them into reliable, reusable outputs.

You'll work 50/50 between Python development and data work: building scripts/services and data pipelines, mainly with Pandas, plus some SQL and PySpark.

What You'll Do Build internal tools and automation around data processing and reporting. Develop and maintain pipelines that ingest data from APIs and large files. Transform and validate data using Pandas; write clean, tested, maintainable Python. Use SQL mainly to extract data and for joins/aggregations when needed. Contribute to large-scale processing (PySpark/Spark) when applicable. Refactor and optimize existing scripts and pipelines; improve performance, readability, and reliability (tests/logging).

What We're Looking For 2+ years of experience with Python in production. Strong Pandas: efficient transformations, joins/merges, memory/performance considerations. Solid engineering habits: tests, code review, debugging, and readable code. Working knowledge of SQL (MS SQL Server is our primary DB, but any RDBMS is fine). Comfortable with Linux and Git; daily tooling skills (e.g., VS Code). Excellent organizational and task management abilities. Self-motivated with the ability to work independently. Good written and spoken English communication skills.

Nice to Have PySpark/Spark and/or Hadoop exposure; distributed processing concepts. Azure experience (storage/compute) or participation in on-prem → cloud initiatives; workflow orchestration is a plus. Data modeling fundamentals and basic ML understanding; а portfolio (GitHub/website) or examples of end-to-end projects/products (clean repo structure, README, tests) is a plus.