Head of Business Intelligence

Tilt Finance

4d•$180,000 - $220,000•Remote

About The Position

We're hiring a leader to run Business Intelligence at Tilt end to end. BI at Tilt is a data modeling, metadata, and context-building function. We sit with teams across the business, learn how they approach their metrics and the questions they're trying to answer, look for commonality across teams, and build models that accurately describe our business. A growing part of the mandate is metadata management: every model and metric gets an AI- and human-readable description, and we maintain a living library of key metrics with reference SQL implementations the rest of the company can trust and build on. BI here is focused on models and tools, not on charts and dashboards. You'll own the function day to day: a team of US and International BI Developers, the data models and semantic layer the business runs on, and the metric-and-metadata library that makes them trustworthy. You'll report to the Head of Business Acceleration and you'll run it. This is a leadership role, but not a hands-off one. You stay close to the craft: you hold the team to a high bar and you understand the long-term impact of the decisions the team makes in the models. You have expert-level SQL, fluency with AI coding tools, and enough depth to read and review dbt work from a team of highly competent BI Developers — you review the work, you don't need to write it yourself. The spike we're testing for is nimble judgment across a wide range of engaged stakeholders. You'll juggle a long, competing list of asks from across the business — and at Tilt that list moves fast, from a change in business priority to an acquisition. The job is knowing which asks actually need attention, which projects are drifting, and where the bar is slipping, then jumping in at the right moment without taking the work over. In addition to our twice-yearly company onsites, travel to Asia is expected for this role 3–4x per year.

Requirements

7+ years in data modeling, analytics engineering, or BI, including 3+ years of managing people directly.
Nimble judgment across engaged stakeholders. You can hold a long, shifting list of asks from teams who all want your attention, tell the most impactful from the rest, and know exactly where to focus.
Steps in without taking over. You jump in at the right moment on the right problem and then get back out — you don't micromanage, and you don't let things drift either.
Expert technical depth. Expert-level SQL, fluency with AI coding tools, and the ability to read and review dbt work from a team of highly competent BI Developers. You know dimensional modeling, semantic layers, and what makes a reporting model accurate and holds up over time. You hold that bar through review, not by taking the keyboard.
Business fluency. You can sit with a team, understand what they actually do and how they measure it, and translate that into shared models that serve them.
A point of view on metadata and context. You treat descriptions, definitions, and a trustworthy metric library as core output, not documentation debt — and you're interested in doing it in a way that's readable by both people and AI.
People leadership. You've managed analysts or analytics engineers, raised a team's bar, and can develop talent while keeping a team moving quickly without letting correctness slip.
Led a team of analysts or analytics engineers, owning both their output and their bar.
Hold a team's quality bar primarily through code review rather than by writing the work yourself.
Expert-level SQL and hands-on depth in data modeling, dbt, and a modern warehouse.
Partnered with business stakeholders to build shared reporting data models.
Managed competing demands from many stakeholders that shifted with the business, and had to prioritize what gets attention and what doesn't.
Owned metric definitions, metadata, or a shared metric library as a deliverable in its own right.

Responsibilities

Own the BI function end to end: the team, the roadmap, and the quality bar.
Own the reporting data models across the business: take each team's view seriously, find the commonality across them, and build shared models that accurately reflect the product or process.
Make metadata part of the product, not an afterthought: AI- and human-readable descriptions on every model and metric, plus a maintained library of key metrics and reference SQL implementations.
Give the whole company a consistent, accurate foundation — every team starting from the same trusted models and definitions instead of their own one-off pulls. BI doesn't run the analysis. It makes the numbers everyone analyzes trustworthy.
Prioritize across a long, shifting stakeholder list: what BI takes on, what it declines, and what needs your attention now.
Grow the team: raise the technical bar and their business fluency, stepping in when it matters and letting them run when it doesn't.