Data & Analytics Engineer

Confirmed live in the last 24 hours

Practice Better

Compensation

$135,000 - $145,000/year

Remote - Canada

Remote

Posted March 31, 2026

Job Description

Ready to make a real impact on global health and wellness care?

Practice Better is an all-in-one platform helping health and wellness practitioners run their businesses, care for their clients, and scale their impact. Founded by practitioners in 2016, we’re now the leading EHR and practice management platform in the wellness industry, trusted by tens of thousands of practitioners across 70+ countries.

In 2023, we expanded our whole-practice care offering through the acquisition of That Clean Life, bringing nutrition planning into our platform.

We’re a remote-first team headquartered in Toronto, made up of curious, driven, and empathetic people building tools that help practitioners create sustainable, independent practices; and do the most meaningful work of their careers. While most of our work happens remotely, we come together regularly for off-sites and team events to stay connected and build together.

Important notice to our applicants and job seekers:

We've become aware of fraudulent messages impersonating Practice Better's recruitment team.

Please note:

Practice Better will never ask for payment, banking details, or personal financial information at any stage of the hiring process
All official communication comes from @practicebetter.io email addresses
We will never ask you to purchase equipment, software, or gift cards
We will never conduct interviews solely via text message or WhatsApp

If something feels off, trust your instincts. Verify any suspicious outreach by contacting us directly at careers@practicebetter.io before taking any action.

Thank you for helping us keep our hiring process safe and transparent.

Practice Better runs a modern data stack (Snowflake, dbt, Stitch, Hightouch) and an internal AI warehouse agent that powers how our go-to-market teams access and act on data. The infrastructure is solid, and we're looking for someone who takes full ownership of it — and, more importantly, deeply understands the data running through it.

This isn't a traditional data engineering role. We don't need someone who only knows how to build pipelines. We need someone who understands how the business works — how Stripe processes transactions, how HubSpot triggers campaigns, how churn is calculated and where the nuance lives — and uses that understanding to make the data layer trustworthy, actionable, and clear.

This is a sole-contributor role on a small, high-output team. You'll own the full stack — pipelines, data models, integrations, and the data layer underpinning our AI-assisted analytics — but your real value is in connecting the dots between systems, data, and the humans who make decisions from it. You'll partner closely with the Head of RevOps, the broader data/analytics team, and stakeholders across the GTM motion.

If you've thrived at a small, fast-moving company where you wore many hats and no one handed you a spec — this is that role, amplified.

What You’ll Be Doing

Understand the Business Through the Data

Develop a deep working knowledge of how our core business systems function — Stripe, Square, HubSpot, and others — not just as data sources, but as interconnected systems with mechanics that shape what the data means
Be the person who can explain why a number looks the way it does, not just where it came from
Partner with stakeholders across Growth, Customer Success, Marketing, and Payments to understand data dependencies, surface issues early, and translate business context into technical requirements
Act as a thought partner and soundboard for the Head of RevOps — challenge assumptions, highlight anomalies, and communicate at a level that drives decisions

Own the Data Stack

Own and evolve the full data infrastructure: Snowflake, dbt, Stitch, Rivery, and pipeline orchestration
Build and maintain ELT/ETL pipelines and dbt models supporting analytics, reporting, and the AI warehouse agent
Manage Snowflake for performance, cost, and reliability
Build and maintain integrations between production systems and the data wa

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