Data Pipeline Engineer en Tritone Analytics, Inc

About Tritone Analytics: Tritone Analytics is a music-technology startup building a forensic royalty auditing platform for the music industry. We help artists, managers, and rights-holders identify unpaid or misreported royalties by combining deterministic data systems with modern AI workflows.

We work with messy, real-world data — distributor reports, royalty statements, contracts — and turn it into structured, queryable systems that power financial analysis and AI-assisted auditing.

Project scope: You will contribute to the core data infrastructure that underpins our platform, focusing on data ingestion, transformation, validation, and the preparation of data for AI workflows. This role sits at the intersection of data engineering, analytical systems, and AI pipelines, ensuring reliable, scalable data processing from messy sources to structured datasets.

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Core Requirements (Must Have): Strong Python for data processing and scripting with real datasets; strong SQL skills (joins, aggregations, validation queries, debugging data issues); proven experience working with messy or inconsistent data; understanding of ETL pipelines and data transformation workflows; ability to debug data issues and explain root causes.

We value curiosity, collaboration, and a bias toward shipping reliable data products. Candidates who enjoy digging into messy datasets, communicating data issues clearly, and partnering with data scientists and engineers to operationalize AI workflows will excel. Prior experience in music rights or financial data domains is a plus.