Job Description

Tritone Analytics is building a forensic royalty auditing platform for the music industry. We ingest messy, real-world financial data from labels, distributors, and publishers—and turn it into clean, structured systems that power analysis and AI workflows.
We’re looking for a junior-to-mid level QA Engineer to own testing across our core data systems.
This is not a traditional QA role. You’ll be working with data pipelines, not just UI flows.
Postula directamente en Get on Board.

Own quality across a Python-based royalty data pipeline (CSV/XLSX → normalized data)
Write and maintain tests across: unit, integration, and E2E (Playwright)
data validation (row counts, column mappings, financial logic)
Expand E2E coverage for our Gradio dashboard (upload → process → export)
Validate transformations using SQL (DuckDB)
Improve and maintain CI/CD pipelines (GitHub Actions)
Identify gaps in test coverage and proactively build solutions
Example Work
Validate that 100K+ rows from a label ingestion pipeline are correctly mapped and normalized
Build tests to ensure financial calculations remain consistent across multiple data sources
Expand end-to-end coverage so ingestion changes don’t break downstream outputs

Must-Have:
1–2 years of experience with Python (pytest)
Comfortable writing SQL queries to validate data
Strong attention to detail—especially with structured data
Ability to work independently in a small team
Strongly Preferred:
Experience with Playwright or browser automation (Selenium is fine)
Exposure to CI/CD tools (GitHub Actions, Jenkins, etc.)
Interest in data pipelines, ETL, or backend systems
Tech Stack:
Python 3.12+, pytest
DuckDB (data processing)
Gradio (UI)
GitHub Actions (CI/CD)
3,000+ existing tests across 100+ files

Nice to Have:
pandas, DuckDB, or data validation tools (pandera, great_expectations)
Experience with financial, analytics, or music-related data
Performance testing or parallelization (pytest-xdist)

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