VeraData

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.