Role Description 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 are looking for a Data Engineer who takes full ownership of it. This is a sole-contributor role on a small, high-output team and because the business moves fast, there's meaningful engineering work queued up from day one for someone who's ready to hit the ground running. You’ll own the full stack: pipelines, data models, integrations with production systems, and the data layer that underpins our AI-assisted analytics. You’ll partner closely with the Head of RevOps, the broader data/analytics team, and stakeholders across the GTM motion. And you’ll bring a genuine, pragmatic excitement about what AI makes possible, not in theory, but in production. Note: Practice Better is a remote-first company with team members across North America. However, this particular role is currently open to Canadian-based candidates only, and preference will be given to applicants legally authorized to work in Canada. What You’ll Be Doing - Own the Full 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 — Stripe, Square, HubSpot, and others — and the data warehouse - Ensure Data Quality & Reliability - Implement data quality monitoring, testing frameworks, and alerting so problems surface before they reach stakeholders - Trace anomalies to root causes and ensure leadership is making decisions on reliable data - Establish and maintain data governance standards: documentation, access controls, and metric definitions - Partner with Product and Engineering to instrument event tracking and ensure complete data capture across the customer journey - Advance AI-Assisted Data Workflows - Design and evolve AI-assisted data workflows, including our internal warehouse agent, ensuring they’re powered by clean, well-modeled, trustworthy data - Bring strong intuition for AI failure modes and how to constrain systems for reliability - Enable stakeholders to self-serve through AI-assisted tooling; your work is what makes that possible - Partner Across the Business - Partner with the Head of RevOps on data model integrity, executive reporting, and prioritizing what gets built or fixed first - Work closely with the analytics team to understand how data is consumed across Sigma, Amplitude, Hightouch, and the AI warehouse agent, so infrastructure decisions account for downstream impact - Partner with GTM stakeholders across Growth, Customer Success, Marketing, and Payments to understand data dependencies, surface issues early, and translate business needs into technical requirements Qualifications - 5+ years as a data engineer, analytics engineer, or backend engineer with a data focus - Experience as the primary or sole data engineer on a small, high-growth team (you’ve owned a full stack end-to-end, not just a slice of one) - Strong SQL and hands-on experience with dbt, Snowflake, Stitch, and Rivery - Python proficiency for pipeline development and automation - Experience building and optimizing ELT/ETL pipelines at scale, with strong understanding of data warehouse architecture and dimensional modeling - A pragmatic approach to AI — focused on outcomes, not demos. You’ve built or worked with AI-assisted data workflows in production, understand their failure modes, and know how to constrain them for reliability - Comfort working with large, imperfect datasets. You diagnose data quality issues independently and don’t wait for someone to hand you a spec - Experience with version control (Git), CI/CD practices, and infrastructure as code - Clear, proactive written communication. You document your work so the next person isn’t starting from scratch Compensation & Pay Transparency At Practice Better, we believe in pay transparency, equity, and fairness. We benchmark compensation against similar-stage, high-growth SaaS companies in both Canada and the United States and review our salary bands regularly to ensure they remain competitive and aligned with market trends. Each role has a defined pay range based on its level, scope, and geographic location. Final offers are determined by several factors, including experience, demonstrated skills, and location, to ensure consistency and equity across our team. Anticipated Base Salary Range (Canada): $135,000 - $145,000/year What We Offer - Comprehensive Benefits - Robust benefits package for full-time, permanent employees, including health, dental, and vision coverage from day 1 - RRSP matching - Generous paid parental leave - Annual learning stipends - Remote-First, Connected Culture - Autonomy and flexibility with optional access to our downtown Toronto office for in-person collaboration - Regular off-sites and team gatherings across North America - Wellness and Growth - Unlimited vacation, built on trust, clear expectations, and real support for taking time off - Company RRSP program with employer-matched contributions - $750 annual Health & Wellness Allowance - $1,000 annual Learning & Development Allowance to support your growth - $500 annual Home Office Allowance to set up a productive remote workspace - Sprout Family: personalized support for family-building and fertility journeys - Inkblot: confidential, digital mental health support from licensed professionals - Company-wide holiday closure in December - Regular virtual company-wide events, lunches, and team socials to stay connected Our Commitment to Diversity, Equity & Belonging We know innovation happens when diverse perspectives come together. Practice Better is committed to fostering an inclusive environment where every team member feels valued, supported, and empowered to contribute their best. If you do not meet every requirement listed above, we still encourage you to apply. Research shows that underrepresented groups often hesitate unless they feel 100% qualified. We are far more interested in your potential, curiosity, and alignment with our values. We are committed to building a workplace where everyone can do the best work of their careers. We welcome applicants of all backgrounds, experiences, and abilities. Accommodations are available throughout the interview process upon request. Ready to Apply? If this sounds like your next step, we would love to hear from you. Join us in shaping the future of health and wellness for practitioners, their clients, and communities worldwide. Apply Now Official Recruitment Notice & AI at Practice Better All applications are reviewed directly by Practice Better’s internal Talent Acquisition team - no bots, no outsourcing. Legitimate communication from our hiring team will only come from email addresses ending in @practicebetter.io or @greenhouse.com. Practice Better will never ask for payment, banking details, or personal financial information during the recruitment process. If you receive suspicious communication claiming to represent Practice Better, please contact our team at careers@practicebetter.io to verify its legitimacy. AI in Recruitment at Practice Better We use AI thoughtfully to take some of the repetitive work off our team’s plate: early drafts of job descriptions, first-round interview questions, and help reviewing larger applicant pools. It’s there to make the process smoother and more efficient for everyone. What AI doesn’t do is make decisions. Every hiring decision is made by real people, using structured and consistent practices designed to reduce bias and ensure fairness. We’re committed to using AI responsibly and reviewing our approach regularly so it stays aligned with best practices, legal guidance, and the kind of candidate experience we want to deliver.

• Build and maintain robust, scalable data pipelines using Airflow, Python, and SQL. • Design, build, optimize data pipelines using dbt, or sqlmesh. • Develop and manage the ODS and support the eventual rollout of a modern Data Warehouse. • Integrate data from internal systems and external APIs to create clean, reliable datasets. • Work closely with engineers to operationalize machine learning workflows. • Ensure high data quality through monitoring, validation, and error handling. • Provide guidance to less experienced team members and champion data engineering best practices. • Deploy and manage infrastructure in the cloud (AWS, GCP, or Azure) using modern DevOps tooling. • Implement monitoring and alerting to ensure data pipelines are reliable and maintainable.

• Analyze and develop data-driven solutions — from analytics and machine learning to GenAI • Design tailored Data & AI strategies for a variety of use cases • Architect future-proof data platforms, ML pipelines and AI systems • Evaluate and implement suitable technologies and guide projects with clear roadmaps • Support pre-sales activities, conduct market analyses and help advance data-driven business areas • Contribute ideas to solution concepts, cost estimates and presentations

Role DescriptionIn this role you will build large, scalable analytics pipelines using modern data technologies. This is not a “maintain existing platform” or “make minor tweaks to current code base” kind of role. We are effectively building from the ground up and plan to leverage the most recent Big Data technologies. If you enjoy building new things without being constrained by technical debt, this is the job for you! Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here. Responsibilities - Help define company data assets (data model), Spark, SparkSQL jobs to populate data models - Help define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage - Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable - Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems - Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way - Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains - Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying. Requirements - 5+ years of Spark, Python, Java, C++, or Scala development experience - 5+ years of SQL experience - 5+ years of experience with schema design, dimensional data modeling, and medallion architectures - Experience with the Databricks platform and data lake architectures for large-scale data processing and analytics - Excellent product strategic thinking and communications to influence product and cross-functional teams by identifying the data opportunities to drive impact - BS degree in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience - Experience designing, building and maintaining data processing systems Preferred Qualifications - 7+ years of SQL experience - 7+ years of experience with schema design, dimensional data modeling, and medallion architectures - Experience with Airflow or other similar orchestration frameworks - Experience building data quality monitoring using MonteCarlo or similar tools Compensation US Zone 1 This role is not available in Zone 1 US Zone 2 $149,200—$201,800 USD US Zone 3 $132,600—$179,400 USD