About the role:
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 & 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.
What you’ll do
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 making sound judgment calls - especially when you can’t get immediate input from the broader team due to timezones.
Nice to have:
Snowflake performance tuning and cost awareness.
Experience with Fivetran, Segment, Amplitude, Hightouch, or similar tools.
Proficiency in Python for automation and scripting.
Familiarity with tracking plans and product analytics concepts.
Experience with reverse ETL workflows.
Exposure to AI-assisted analytics workflows or building lightweight internal automations.
What success looks like:
In the first 90 days, we’d expect to see faster resolution on support requests, clearer diagnosis of root causes, and at least one meaningful contribution to team productivity or data quality. Stakeholders should feel well-supported, with clearer answers, fewer recurring definition disputes, and more confidence using shared metrics.
Why join us:
Join our global SaaS scale-up company where we foster a collaborative, open, and transparent culture and work without ego. Our team comprises curious and intelligent colleagues who embrace challenges. Here's what we offer:
Experience comes in many forms, and skills are transferable, please consider applying, even if you don’t tick all the boxes.
Ignition is an equal opportunity employer, providing fair consideration to all applicants regardless of background.
Ignition is committed to providing accommodations throughout the hiring process. If there’s a fit, we’ll work with you to meet your accessibility needs. For questions, requests, or alternate formats, contact us at [email protected].
Compensation:
Our salary bands are designed to reflect the range of skills and experience needed for the position and to allow room for growth. The final offer amount for this role will be dependent on state/location, applicable experience, and skillset of the candidate.
For experienced individuals, we typically hire at or around the midpoint of the band. The top portion of the salary band is reserved for employees who demonstrate sustained high performance and impact over the course of their tenure at Ignition.
This role has a minimum annual base of CAD $120,000 and maximum annual salary of CAD $160,000 (plus equity, health insurance & other wellness perks).
Careers at Ignition. Find Great Talent with Career Pages. | powered by SmartRecruiters | Find Great Talent with a Career Page.
Please mention you found this job on AI Jobs. It helps us get more startups to hire on our site. Thanks and good luck!
Understand the required skills and qualifications, anticipate the questions you may be asked, and study well-prepared answers using our sample responses.
Analytics Engineer Q&A's