Job title: Python Data Engineer
Company: Legitary GmbH
Job description: Legitary GmbH is an award-winning AI startup – we offer audit and valuation services to ensure artists and music right holders receive fair and accurate streaming royalties. As a Python Data Engineer at Legitary, you will structure, normalize and analyze large-scale client datasets to generate clear, data-driven insights that help our clients understand and optimize their streaming revenues.Tasks– Normalize large data sets (at TB scale & beyond) of music streaming usage data from heterogenous data sources– Run data analyses with internal ML apps– Perform data exploration on large data sets & be able to generate new insights from the data– Report insights to clients & stakeholders– Design new product features based on customer requirementsRequirements– Degree in mathematics, statistics, informatics or elated fields– Industry experience (Python & SQL) with a proven track record of data engineering projects– Hands-on experience in data wrangling and manipulation– Strong communication skills for reporting results to technical & non-technical audiences– A glimpse into our Tech Stack: Dask, Plotly & Dash, SQL, BigQuery, Kubernetes, GitlabBenefits– International career opportunities– Dealing with top-tier music industry professionals– Working on the interface of latest ML development– Creative leeway for your ideas & their implementationJoin Legitary as a Python Data Engineer and innovate the music industry with AI. Make an impact in a dynamic startup environment in Vienna. Apply now and transform music streaming!Legitary GmbHLegitary is an award-winning music-tech startup powering the monetization of music IP. With our patented AI tools we revolutionize streaming auditing & music IP valuation for music labels, publishers, artists and M&A companies.Join our mission to bring fairness & transparency to the world of music streaming, leveraging the power of advanced AI technology.
Expected salary: €45000 per year
Location: Wien
Job date: Sun, 06 Jul 2025 22:53:15 GMT
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