Data Engineer helping to improve ETL processes for investment analyses at The Battle of Giants. Collaborating directly with leadership to shape strategies and insights.
About the role
- Data Engineer owning the full data stack for Practice Better, a health and wellness platform. Collaborating with teams to ensure reliable data and developing AI-assisted workflows.
Responsibilities
- 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
- 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
- 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 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
Requirements
- 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.
Benefits
- Comprehensive health and dental benefits from day 1
- RRSP matching
- Generous paid parental leave
- Annual learning stipends
- Unlimited vacation
- $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
- Personalized support for family-building and fertility journeys
- 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
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