We’re looking for an Analytics Engineer to join as an early member of our Data Team and help build the systems that power artist sourcing, track pricing, portfolio management, and catalog growth
This role sits at the intersection of analytics, engineering, and product with high autonomy, technical ownership, and direct business impact
You will report to the Head of Data & Analytics and help define the future of our data products
Because data fuels everything we do, the Data Team works horizontally across the entire company
While your core focus will be deal-flow optimization and catalog pricing - the core of Duetti’s business model - you’ll also collaborate with music-industry veterans, financial experts, and senior engineers and data scientists across a variety of initiatives
Own the core models, transformations, and analytics products that drive track and catalog pricing decisions
Partner with Finance & Management to uncover insights into catalog performance, revenue drivers, and portfolio health
Collaborate with Data Science to refine predictive models and integrate third-party and proprietary datasets
Work closely with Product to design internal tools, workflows, and decision support systems
Identify and resolve data quality and pipeline issues, ensuring a reliable analytics foundation across teams
Dive deep into streaming performance and catalog behavior to uncover optimization opportunities for acquired catalogs
Explore and build LLM-powered internal tools that automate data workflows
Ability to translate complex data into clear, actionable insights for technical and non-technical stakeholdersHands-on experience with BI tools (Looker, Tableau, or equivalent)Strong SQL & Python skills for data modeling, transformation, and automationA builder mindset: autonomous, curious, proactive, and excited to shape a fast-moving analytics stack5+ years in analytics engineering or data analytics roles within high-growth environmentsExperience with music streaming data, KPIs, or creator-economy platforms (preferred but not required)Experience collaborating on or implementing LLM-powered internal tools or workflows (preferred but not required)
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