Head of Data

7shifts•Toronto, ON

1d•CA$210,000 - CA$260,000•Hybrid

About The Position

As Head of Data at 7shifts, you'll own the data function end-to-end: the operating model, the platform investment, the team, and the outcomes. Reporting to the CTO, you'll lead a multi-disciplinary group across data engineering and analytics, working closely with Product, Revenue, Finance, and GTM Data Science to make data central to how the business operates day-to-day. This role spans two big areas of investment. One is the foundation: a warehouse, semantic layer, and modeling discipline the business trusts. The other is activation: models that ship into CRM, lifecycle, and operational systems where data turns into revenue. AI is not a side project here, it's how this function will need to evolve: agents that monitor pipelines, models that score accounts, and workflows that push insight into the systems where work actually happens. You’ll be leading the charge in building this with your team.

Requirements

10+ years in data, with at least 3 years leading a multi-disciplinary data function (engineering, analytics, science) in a B2B SaaS company at our stage or larger.
A track record of building the foundation, not just consuming it. You've stood up or rebuilt a warehouse, semantic layer, and modeling discipline that the business actually uses. You understand why most "self-serve analytics" projects stall, and you have a point of view on how to make this one work.
Strong technical foundation. You understand the semantic layer and modern data stack well enough to make platform calls without outsourcing them. You can read a dbt model, debate a schema design, and challenge an architecture choice.
A track record of shipping activation, not just reporting. You've put models into production systems and can show the revenue impact, not just the F1 score.
People leadership that develops, not just manages. You set clear expectations, run 1:1s with intent, and people who've worked for you can point to how they grew. You've hired well and performance-managed when needed.
Comfort writing code or building automations yourself. SQL, Python, dbt, LLM workflows, agent frameworks. You stay close enough to the work to ship something when the team can't get to it.
Comfort operating without a fully built foundation. You can build the plane while flying it and stay calm doing it.
A point of view on data strategy, platform investment, team structure, and where the business should be measuring itself. You bring opinions to the table, tell the truth fast, and back it with data.

Nice To Haves

Restaurant, multi-location SMB, or hospitality-tech experience is a strong plus.
Experience in companies that run both PLG and sales-assisted motions in parallel.
Experience standing up a data function inside a business moving from "lots of dashboards" to "models in production."

Responsibilities

Set the operating model for data: Decide how requests flow, what's self-serve versus analyst-led, and how the platform team works alongside the activation team. Bring a clear point of view, run the model with discipline, and revisit it as the business scales
Make the foundation trustworthy: Own the warehouse, pipelines, semantic layer, modeling, and governance that everything else reads from. The bar is simple: when a leader looks at a number, they trust it. When the same metric appears in two places, it means the same thing in both
Make self-serve real: Most data questions in the business shouldn't need a ticket. You'll decide what gets built once and consumed without friction, who owns the user experience on top of our BI tool, and how business context lives inside the metrics so the answers are right
Push data into the business, not just into dashboards: Build the team and workflows that take models (ICP scoring, churn, next-best-action, etc) and ship them into Salesforce, lifecycle, and operational systems. This is where data turns into revenue, and you'll close the gap between insight and action with measurable impact on pipeline, retention, or ARPL
Build a team that operates with business context, not just technical skill: Analysts know the function they serve. Data engineers know what the analysts need. Data scientists know what the business will act on. You'll set the structure and meaningfully develop the team
Stay hands-on: Success in this role comes from operating in the details. You'll read dbt models, pressure-test schema designs, sketch architecture before passing it off, and write code when the team can't get to something. You're senior enough to set direction and close enough to the work to know when it's right
Run the AI playbook: You'll set the standard for how the team uses LLMs, agents, and automation in daily work. You won't tell them to "try AI." You'll show them how, ship workflows yourself, and develop the team to operate in this way by default
Be the partner the rest of the org needs: Tight loop with Product on what we ship and how we measure it. Tight loop with GTM on pipeline, retention, and ICP. Tight loop with Finance on the numbers that go to the board. You're at the table where decisions get made