Hire Data Engineer for Music Artists
Senior, direct, no layers

As a music artists business grows, manual reporting scales badly: more sources, more spreadsheets, more versions of the truth, and shows announced before tickets are linked discovered weeks after it started. The pipeline approach scales differently, because each new source is modelled once and then serves every report.

What the business already records is the raw material. ticketing sales and merch store orders may be messy, but they are real, and modelled carefully they answer streams and saves by track and market and merch revenue per show without new data collection.

Rollout is deliberately narrow: one pipeline, one report, one decision at a time. Each piece must prove the number is trusted before the next source is added, which is why email campaign results feeding streams and saves by track and market usually comes first.

The warehouse draws on email campaign results, streaming platform data, ticketing sales, merch store orders, and social engagement data. Each source is pulled on a schedule, checked for duplicates and missing records, and modelled into tables the business can query, so streams and saves by track and market, ticket sales pace by city, and merch revenue per show all read from the same definitions instead of separate extracts.

The existing stack here usually means distribution platforms (releases and streaming), ticketing platforms (shows), merch platforms (store sales), social media (audience and content), and email platform (fan list). Each of those systems holds part of the picture. The build connects them into one modelled layer, so a report draws from the warehouse rather than from five separate exports.

A sensible first project is one pipeline done properly, often email campaign results feeding streams and saves by track and market, with the team using the resulting report for real decisions before anything else is added. A pipeline that proves itself on one decision earns the trust to carry the rest.

Take email campaign results. Today it probably arrives as an export someone cleans by hand. In the pipeline it is pulled on a schedule, checked for missing or duplicate records, and modelled into the same definitions every report uses, so streams and saves by track and market is a number the team trusts instead of a spreadsheet they debate.

distribution platforms handles releases and streaming. ticketing platforms handles shows. merch platforms handles store sales. social media handles audience and content. email platform handles fan list.

What changes in practice is where numbers come from. When email campaign results and streaming platform data flow through tested pipelines into one modelled layer, the team stops rebuilding reports and starts acting on them. The time saved is real, but the trust is the actual deliverable.

That also changes how the business argues: disagreements move from whose spreadsheet is right to what the number means and what to do about it, which is where a music artists team's judgement actually adds value. Browse all hire engagements or discuss your data setup directly.