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We’re hiring an Analytics Engineer to help transform how we understand, model, and scale the data behind our product and business. You’ll work across Product, Engineering, Science, and GTM teams to turn raw information into the reliable, actionable insights that power Suno’s next generation of creative tools.
Job Responsibility:
Partner with Product, Science, Engineering, and GTM teams to understand data needs, define key metrics, and translate business requirements into scalable data models
Design, build, and maintain dbt transformations and ETL pipelines that turn raw data into reliable, analytics-ready tables
Implement data validation, testing, and monitoring frameworks to ensure accuracy, consistency, and timeliness of core datasets
Build and maintain dashboards and reports in tools like Hex, Looker, or Mode, giving teams real-time visibility into product and business performance
Collaborate with stakeholders to define, instrument, and operationalize the metrics that inform strategic decisions across teams
Drive data democratization by developing reusable data products, documentation, and self-serve tooling
Partner with Engineering, Science, and Product to ensure data instrumentation and event tracking align with analytics needs
Champion a data-first culture by influencing how the organization collects, models, and interprets information
Requirements:
5+ years of experience in Analytics Engineering, Data Analytics, or Data Science roles, ideally supporting Product and GTM teams
Deep expertise in SQL and strong proficiency with dbt for modular, scalable data transformations
Experience with Airflow (or similar orchestration tools) and version control systems like GitHub
Proficiency in data processing, automation, and validation
Hands-on experience building metrics reporting, dashboards, and analytical tools in platforms like Hex, Mode, Looker, or Tableau
Proven ability to translate messy, complex data into clear insights that drive real impact
A bias for action and pragmatism — delivering high-quality data solutions without overengineering
A “full-stack data” mindset, comfortable across collection, modeling, validation, and visualization layers
Experience helping build or scale an Analytics Engineering or Data Platform function at a startup or fast-growing organization
A strong sense of ownership, curiosity, and comfort with ambiguity in a fast-moving environment
A genuine passion for music, creativity, and AI — and excitement to help enable the next wave of generative creation