Data Analyst

Meter sells networks the way utilities sell power: as something that just works. Behind that promise is a business growing fast across enterprise customers, multi-site deployments, and a partner ecosystem.

Right now, the data that runs the business is scattered across a dozen systems that don't talk to each other. This role fixes that.

Why this role matters

Every important decision at Meter — where to spend marketing dollars, which accounts to prioritize, how to forecast next quarter — is only as good as the data underneath it. Today, that data is fragmented across Salesforce, HubSpot, Stripe, ad platforms, partner systems, and product telemetry, and every team rebuilds its own version of the truth. We need one person to own the layer that turns those signals into something the company can actually run on.

What you'll do in your first six months

• Ship canonical dbt models for accounts, opportunities, marketing touches, and revenue that finance, sales, and marketing all use — replacing the four versions of "ARR by segment" floating around today.
• Cut the time it takes the marketing team to answer "is this channel working" from a week of manual reconciliation to a query.
• Build the attribution and funnel layer that lets us actually compare the cost of acquiring a customer through partners versus paid versus outbound.
• Become the person GTM leadership goes to when they don't trust a number — and the person whose work makes that question rarer over time.

What you'll do in your first year

• While you're picking up quick wins, the first few months are about laying the foundation. The next are about using it.
• You'll embed with finance during forecasting cycles and with marketing during budget planning. You'll be in the room when sales leadership is debating territory coverage. The business models you built in month four will be the substrate for an attribution rebuild in month nine. By the end of year one, you'll have set the standard for analytical rigor at Meter; the bar that the next five analysts we hire will be measured against.

What a typical week looks like

Monday morning you're pairing with a marketing lead on why their LinkedIn spend report doesn't match what finance recognized last quarter. Tuesday you're shipping a dbt PR that consolidates three definitions of "active customer" into one. Wednesday you're in the forecast review, watching the head of sales argue about coverage ratios, and you realize the underlying data has a fanout problem you can fix by Friday. Thursday is deeper IC work: designing the schema for a new partner data source.

Friday you're reviewing a teammate's model and writing the test that catches the next regression before it ships.

What we're looking for

• You've been the analytical partner inside a GTM function — not just the analyst who delivered reports to one. You've sat in pipeline reviews, argued about attribution definitions, and built dashboards that executives actually use.
• You write SQL the way most people write English. You reach for window functions, CTEs, and set operations without thinking. You can read someone else's 200-line query and find the bug in ten minutes.
• You think in dbt. You have opinions about staging vs. marts, when to use incremental models, and what belongs in a snapshot. You've designed schemas that survived contact with a changing business.
• You've worked across Salesforce, billing systems, marketing platforms, and product data, and you understand how they're each subtly wrong in their own way.
• You can take "is our marketing spend working?" and turn it into a structured analysis with a clear, defensible answer — including what you're not sure about.
• You build trust before you ship models. You know the dashboard nobody uses is worse than no dashboard at all.
• You have deep experience with one or more of the following: Snowflake, BigQuery, Tableau, or other modern data stacks.
• Why Meter?
• The internet runs the world. Every purchase you make, video call you join, it's all packets flowing through networks. But those networks haven’t changed for decades. They’re brittle, complex, and surprisingly hard to set up in an enterprise space.
• We started Meter to build better networks. We had to build everything from the ground-up: designing and building our own enterprise hardware, intuitive software, and streamlined operations to deliver great outcomes for our customers. Today, we build and deploy these networks at scale. Ambitious companies and enduring institutions like Bridgewater, Lyft, Reddit, rely on Meter to keep their thousands of employees and locations online and productive.
• Our bet with Meter is simple: we will all use the internet more than we do today. We believe we have the definitive networking stack in place to enable business to do so as seamlessly and reliably as any modern utility.