Analytics Intern

Zello is a voice-first communication platform, powered by industry-leading push-to-talk technology, to improve collaboration and productivity for desk-less workers. With over 175+ million users, we’re the #1 rated push-to-talk app in the world, delivering 9 billion messages a month. Our company values are at the heart of what we do, serving frontline workers, connecting people in times of crisis, and supporting first responders.

About the role

Our go-to-market team should be able to answer their own data questions without waiting on us, but right now they can't: dashboards are slow, definitions don't line up, and most numbers still need someone from the data team to explain them. You’ll join the Data & AI team in Austin, reporting to our Head of Analytics, and spend the fall fixing that for the reports and topics people use most. This is real ownership, not shadowing. By the end of your internship, you will have:

• Rebuilt three or more of the dashboards our go-to-market team opens every week, so they load fast, use governed definitions, and no longer need someone from the data team standing next to them
• Shipped and documented a set of Omni topics that a salesperson or Customer Success can answer their own questions from, tested against the questions they actually ask rather than the ones we assumed they’d ask
• Published an inventory of the analytics surfaces you worked on, each with an owner and a clear keep, consolidate, or retire recommendation
• Turned a handful of small analyses around end to end, from a fuzzy question to a written recommendation someone acted on
• Left documentation good enough that the next person picks up your work without booking time with you

Responsibilities

• Rebuild and speed up dashboards in Omni, our BI tool, starting with the ones people complain about most
• Build Omni topics so non-technical teammates can ask their own questions, then sit with those teammates and watch where the topic falls over
• Write SQL against Snowflake to pull, check, and reconcile numbers, and read our dbt models to trace where a metric actually comes from
• Take small analysis requests end to end: scope the question, pull the data, and come back with a recommendation rather than a spreadsheet
• Document what you build and inventory what already exists, including the honest calls about which reports should be retired
• Use AI tools, Claude included, to move faster on the routine parts, and check what they hand back before it goes anywhere
• Show your work in team meetings, and say so when a number looks wrong

Requirements

• Currently enrolled in an undergraduate or graduate program, and able to work about 20 hours a week on-site in Austin around your class schedule through early December
• Write your own SQL against a real database, joins and aggregations included, and can debug a query that returns something surprising
• Explain what a number means to someone non-technical, and trace it back to where it came from
• Check your own work, so when you hand something over you’ve already found the errors you were going to find
• Taken at least one project past what was assigned, and can say what changed because you did
• Write things down, so whoever picks up your work after you doesn’t need to book time with you first
• Already use AI tools for real work, and verify what they give you instead of trusting it

What this role is not

• An ML or AI research internship — this is analytics and BI work
• A data engineering role — ingestion and pipelines are owned by the team
• A semester of shadowing — you’ll own real surfaces other people depend on, with review, not observation
• Fully remote — this is hybrid in Austin, and the in-person time is where most of the useful context transfers

Benefits

• Competitive pay and equity with significant upside
• Intentionally designed benefits to encourage healthy and well-balanced employees, flexible schedules, and time off
• Sabbatical after every five years of service
• Ping-pong table and free snacks in the break room