Job Description

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.
Postula exclusivamente en getonbrd.com.

Build and maintain pipelines that transform messy CSVs, metadata exports, and contracts into structured datasets.
Design and enforce canonical schemas across inconsistent data sources to enable reliable analytics.
Write SQL to validate outputs, reconcile datasets, and support financial analysis.
Debug and improve data quality across ingestion and transformation stages.
Support document ingestion workflows (chunking, preprocessing, metadata tagging).
Help prepare structured inputs for LLM-based workflows (RAG, extraction, classification).
Improve reliability of pipelines (error handling, logging, testing).

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.

Nice to Have
: Experience with DuckDB, Polars, Pandas, or PyArrow; familiarity with Parquet or columnar data formats; exposure to vector databases or RAG systems; experience handling large CSV datasets or financial data; basic understanding of LLM workflows.

Benefits to be discussed at time of conversion to a full-time role.
We offer a collaborative, founder-led culture with an emphasis on curiosity, continuous learning, and shipping impactful data products. Competitive compensation, flexible work hours, and opportunities for professional growth in a rapidly evolving music-tech space. Our team is distributed; we value autonomy and ownership over your projects. We support conference attendance, training, and peer knowledge sharing. We look forward to discussing how Tritone can support your career trajectory.