Data Analyst

Meter•San Francisco, CA

About The Position

Meter sells networks the way utilities sell power: as something that just "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. 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. In the first six months, you will 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. You will cut the time it takes the marketing team to answer "is this channel working" from a week of manual reconciliation to a query. You will build the attribution and funnel layer that lets us actually compare the cost of acquiring a customer through partners versus paid versus outbound. You will 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. While 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. A typical week looks like this: 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.

Requirements

Been the analytical partner inside a GTM function — not just the analyst who delivered reports to one.
Sat in pipeline reviews, argued about attribution definitions, and built dashboards that executives actually use.
Write SQL the way most people write English. Reach for window functions, CTEs, and set operations without thinking. Can read someone else's 200-line query and find the bug in ten minutes.
Think in dbt. Have opinions about staging vs. marts, when to use incremental models, and what belongs in a snapshot. Designed schemas that survived contact with a changing business.
Worked across Salesforce, billing systems, marketing platforms, and product data, and you understand how they're each subtly wrong in their own way.
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.
Build trust before you ship models. Know the dashboard nobody uses is worse than no dashboard at all.
Deep experience with one or more of the following: Snowflake, BigQuery, Tableau, or other modern data stacks.

Responsibilities

Ship canonical dbt models for accounts, opportunities, marketing touches, and revenue.
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.
Embed with finance during forecasting cycles and with marketing during budget planning.
Be in the room when sales leadership is debating territory coverage.
Design the schema for a new partner data source.
Review a teammate's model and write the test that catches the next regression before it ships.