Data Analytics Engineer

Overview

Who we are: Founded in 2013, Ignition is the leading revenue generation platform for accounting and professional services businesses to spark greater efficiency and profitability. Ignition automates and optimizes proposals, billing, payments and workflows in a single platform that fits seamlessly into existing technology stacks. With a vision to transform how professional services and their clients do business together, Ignition empowers 7,250+ businesses to reach their full revenue potential.

To date, Ignition customers have engaged over 1.7 million clients and generated US $8b in revenue via the platform. Ignition’s global workforce spans Australia, Canada, New Zealand, the Philippines, US and the UK.

Company Values:

• We are better everyday
• We work without ego
• We are smarter together
• We hero our customer

Role

Location:

We are open to any candidates with the right work in Toronto, Canada or Vancouver, British Columbia.

We’re hiring an Analytics Engineer to improve how the business accesses and uses data—through durable modeling, a strong semantic layer, and stakeholder enablement. Reporting to the Head of Data and Analytics, you’ll work closely with stakeholders while collaborating with a globally distributed data team. This role focuses on excellence in data modeling and semantic layer design, ensuring people (and AI-assisted workflows) can access reliable, well-structured data.

A meaningful portion of the role involves support and investigation work: diagnosing issues, clarifying requirements, and unblocking stakeholders. Our core data team is based in Australia, and this role is based in North America. You’ll be a key partner for North American stakeholders, often serving as the primary data contact for those based in Eastern and Pacific timezones, while collaborating with the wider data team in Australia during overlapping hours.

The semantic layer work has a dual purpose: enabling business users today, and laying the foundation for AI-assisted analytics over time. Clear metric definitions, well-structured models, and strong documentation are what make both human self-service and AI-driven analytics possible.

• Design, build, and maintain data models in dbt, with attention to grain, fact types, and modeling best practices. Default to fixing problems at the right layer, even when a shortcut exists.
• Develop and maintain the semantic layer in Looker—standardized metrics and reusable Explores over one-off fields—to support scalable analytics and AI-assisted workflows.
• Partner with stakeholders to deliver scalable solutions—prioritizing reusable metrics and Explores over one-off artifacts, unless a bespoke solution significantly unblocks adoption.
• Conduct end-to-end investigations when issues arise across the data stack (ingestion, modeling, BI, reverse ETL).
• Work across adjacent tools including Snowflake, Segment, Amplitude, and Hightouch as needed.
• Identify opportunities to improve data quality, reduce costs, and simplify the codebase.
• Use AI tools daily for code generation, investigation, and documentation. AI is a draft; you own correctness through review and validation.
• Enable AI-driven analytics (e.g., Snowflake Cortex, Looker conversational analytics) by optimizing the semantic layer and curating high-quality context for these systems.
• Champion engineering best practices: version control, testing, clear documentation, and well-contextualized pull requests.

What we’re looking for

Required:

• Strong understanding of data modeling fundamentals: facts vs dimensions, grain, slowly changing dimensions, immutability.
• Advanced SQL skills on a modern data warehouse platform (we use Snowflake).
• Experience contributing to production analytics codebases (e.g., dbt) with reviews, testing, and conventions.
• Comfort working in Looker or similar BI tools, with a focus on reusable patterns rather than one-off solutions.
• Ability to engage proactively with stakeholders and communicate in business terms.
• Willingness to spend time clarifying problems before jumping to solutions.
• Daily use of AI tools, with appropriate review and validation of outputs.
• Comfort working autonomously and…