Hire Data Engineer for Entertainment
Senior, direct, no layers

A entertainment business usually has the data it needs, but not in one place. Ticketing sales records, streaming viewing data, and email campaign results live in different systems, reports are rebuilt by hand each month, and decisions are made on numbers nobody fully trusts. When tickets unsold until the last week keeps happening, the cause is rarely a lack of data; it is that the data never gets collected, cleaned, and presented in a form anyone can rely on.

The engagement covers shows, events, streaming content, and live experiences as a data problem: what the business measures, where that data lives, and what decisions it should support. The calendar is the product: shows booked, tickets on sale, audiences marketed to before opening night, and a second weekend that depends entirely on the first weekend's word of mouth. Reporting built on that reality gets used in weekly meetings; reporting built on generic metrics gets ignored.

The work is done inside the business's own systems, with definitions agreed before any dashboard is built. A metric means the same thing in every report, because the model defines it once. That discipline is the difference between reporting the team trusts and reporting the team argues with.

The warehouse draws on ticketing sales records, streaming viewing data, email campaign results, social engagement data, and merchandise sales. Each source is pulled on a schedule, checked for duplicates and missing records, and modelled into tables the business can query, so ticket sales pace against on-sale dates, repeat attendance rates, and audience segment response all read from the same definitions instead of separate extracts.

Day to day, the business already runs on ticketing platforms (sales and seating), streaming analytics (viewing data), email platforms (audience campaigns), social media (show marketing), and CRM (audience segments). Pipelines pull from those sources on a schedule, models define the metrics once, and the reporting layer serves every team from the same definitions.

A sensible first project is one pipeline done properly, often ticketing sales records feeding ticket sales pace against on-sale dates, 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 ticketing sales records. 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 ticket sales pace against on-sale dates is a number the team trusts instead of a spreadsheet they debate.

ticketing platforms handles sales and seating. streaming analytics handles viewing data. email platforms handles audience campaigns. social media handles show marketing. CRM handles audience segments.

The measure that matters is use months later. Pipelines still running on schedule, reports still opened weekly, and new sources added without rebuilding the warehouse is the deliverable. Anything that needs its builder to survive was not finished.

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 entertainment team's judgement actually adds value. Browse all hire engagements or discuss your data setup directly.