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. What You’ll Do 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) #J-18808-Ljbffr