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Internship Music Data Analytics Jobs (NOW HIRING)

EV Realty

Intern, Data Analytics

San Francisco, CA · On-site +1

The Role As a Data Analytics Intern you will help build and maintain various analytics tools and ... This position is a summer internship. The ideal candidate will be a self-starter, a natural problem ...

EV Realty

Intern, Data Analytics

San Francisco, CA · On-site

The Role As a Data Analytics Intern you will help build and maintain various analytics tools and ... This position is a summer internship. The ideal candidate will be a self-starter, a natural problem ...

Umusic

Data & Analytics Engineer, ServiceNow

Nashville, TN · On-site

$110K - $132K/yr

We are the world's leading music company. In everything we do, we are committed to artistry ... How we LEAD: The Data & Analytics Engineer, ServiceNow is responsible for delivering trusted ...

Trinity Industries

Data Analytics Intern

Dallas, TX · On-site

Trinity Industry is looking for Data Analytics Interns for our office in Dallas, TX . This position will focus on building, deploying, and scaling agentic AI applications and automated workflows ...

TrinityRail

Data Analytics Intern

Dallas, TX · On-site

Trinity Industry is looking for Data Analytics Interns for our office in Dallas, TX . This position will focus on building, deploying, and scaling agentic AI applications and automated workflows ...

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How much do internship music data analytics jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for internship music data analytics in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is the difference between Internship Music Data Analytics vs Music Data Analyst?

AspectInternship Music Data AnalyticsMusic Data Analyst
Required CredentialsEnrolled in or recent graduate of relevant programBachelor's or higher in data science, analytics, or related field
Work EnvironmentInternship setting, learning-focused, entry-levelFull-time professional role, project-driven
Employer & Industry UsageMusic companies, labels, streaming services for trainingMusic industry companies, analytics firms, streaming platforms

Internship Music Data Analytics positions are entry-level, focused on learning and gaining experience in music data analysis. Music Data Analysts are full-time professionals responsible for analyzing music data to inform business decisions. Internships serve as a stepping stone toward a career as a Music Data Analyst, which requires more experience and advanced skills.

What types of projects or tasks can I expect to work on during a music data analytics internship?

As a Music Data Analytics intern, you can expect to work on projects such as analyzing streaming trends, developing data visualizations, and supporting the evaluation of listener demographics for marketing or A&R teams. You'll likely collaborate with other analysts, data scientists, and sometimes product managers or marketing teams to extract insights from large music datasets. Typical tasks might include cleaning and organizing data, building dashboards, and helping to generate reports that inform business or creative decisions. This hands-on experience will give you an understanding of both the technical and strategic side of data analytics within the music industry.

What are the key skills and qualifications needed to thrive as an internship in music data analytics, and why are they important?

To thrive as an Internship Music Data Analytics, you need a solid grounding in statistics, data analysis, and familiarity with the music industry, often supported by coursework in data science or music business. Proficiency with tools such as Excel, SQL, Python, and data visualization platforms like Tableau is commonly expected. Strong analytical thinking, attention to detail, and effective communication skills help interns interpret data and present insights clearly. These abilities are crucial for transforming complex music data into actionable strategies that support business decisions.

What is an internship in music data analytics?

An Internship in Music Data Analytics is a temporary position, often for students or recent graduates, where you work with music industry data to uncover trends, patterns, and insights. Interns typically analyze streaming numbers, audience demographics, and social media metrics to help artists, labels, or platforms make data-driven decisions. The role combines analytical skills, knowledge of music, and familiarity with data tools like Excel, Python, or specialized analytics platforms. This experience is valuable for those interested in music business, data science, or both.
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Internship Music Data Analytics jobs near you
Infographic showing various Internship Music Data Analytics job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Director, Data Analytics & Automation

6AM City, LLC

California, MO • On-site

$180 - $250/hr

Other

Posted 2 days ago

New

Job description

At Warner Chappell Music (WCM) - the global music publishing arm of Warner Music Group (WMG) - we shape the culture of songwriting and champion songs that resonate for generations by creating transformational opportunities for songwriters everywhere. Our people and culture are at the epicenter of our company, and we are looking for team members who share our service-oriented mindset and have a passion for looking after our songwriters.

Job Title: Director, Data Analytics & Automation

Reports To: VP, Data Analytics & Automation

Department: Strategy, Integration and Operations

Location: Los Angeles

A little bit about our team:

The Warner Chappell Global Strategic Integration and Operations team is a small but mighty team focused on delivering and implementing business objectives. Working closely with our global leaders, we are responsible for driving and embedding our global business strategy.

The Data Analytics & Automation team sits at the intersection of business strategy, data products, analytics, and operational automation. We partner across all core WCM business domains, regional teams, and global leadership to turn complex business questions into reliable reporting, scalable data models, workflow tools, and automation that improve decision-making and reduce manual effort.

Your role:

As the Director, Data Analytics & Automation, you are a senior player-coach responsible for leading a high-performing team across both data analytics and automation. You will help define and execute the roadmap for modern data products, workflow automation, AI-enabled development, and self-service analytics across Warner Chappell. This role requires strong business judgment, hands‑on technical fluency, people leadership, and the ability to translate ambiguous stakeholder needs into practical solutions that are scalable, governed, and adopted by the business.

You will lead analysts and automation specialists who build across dbt, Snowflake, Dagster, Streamlit, Airtable, AI‑assisted development tools, and related modern data and workflow technologies. You will be expected to shape priorities, mentor talent, raise technical standards, manage cross‑functional delivery, and ensure the team is building durable assets rather than one‑off outputs.

You will be expected to shape priorities, mentor talent, raise technical standards, manage cross‑functional delivery, and ensure the team is building durable assets rather than one‑off outputs.

Here you'll get to:
  • Lead a multidisciplinary data and automation team: Manage, mentor, and develop analytics and automation talent, fostering a culture of business partnership, technical excellence, ownership, documentation, and continuous improvement.
  • Own the strategic roadmap for analytics and automation: Partner with the VP, Data Analytics & Automation and global stakeholders to define priorities across data products, workflow automation, platform modernization, AI‑assisted development, and self‑service reporting.
  • Translate business ambiguity into practical solutions: Work with senior stakeholders across all core WCM business domains, regional teams, and global leadership to clarify problems, identify decision points, and shape the right analytical or automation approach.
  • Drive delivery of durable data products: Oversee the design, development, testing, deployment, and adoption of reusable dbt models, governed Snowflake assets, scheduled Dagster jobs, Streamlit applications, Airtable workflows, dashboards, and recurring reporting products.
  • Advance platform modernization: Help move the organization away from fragile legacy reporting, manual extracts, disconnected spreadsheets, and one‑off workflows toward maintainable data models, governed reporting layers, repeatable automation, and scalable product patterns.
  • Establish standards and governance: Develop and reinforce best practices for data modeling, code review, documentation, access control, scheduling, quality checks, stakeholder handoff, AI‑assisted development, and production support.
  • Lead high‑impact analytical initiatives: Guide work that supports business‑critical decisions, operational improvements, revenue opportunities, rights and repertoire strategy, financial visibility, and scalable reporting across WCM's core business.
  • Champion automation and AI‑enabled workflows: Identify opportunities to use automation and modern AI tools to accelerate delivery, reduce manual operational burden, improve business processes, and help the team prototype responsibly without sacrificing reliability.
  • Build strong cross‑functional relationships: Act as a trusted liaison between business teams, technology partners, data platform stakeholders, and regional leaders to ensure solutions align with business priorities and technical architecture.
  • Communicate impact to leadership: Regularly report on roadmap progress, delivery health, adoption, risk, operational improvements, and measurable business impact for analytics and automation initiatives.
  • Develop future leaders: Coach team members on stakeholder management, technical judgment, prioritization, delivery discipline, and how to turn repeat r
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