Lead Data/Analytics Engineer

Own the event pipeline from instrumentation through Segment into Amplitude, Iterable, and Snowflake, including sources, destinations, and reverse ETL

Define and publish a typed event schema and governance workflow that frontend teams build against, so event data stays consistent across web and mobile

Design and validate tracking plans for new features and surfaces, and confirm events flow end to end before launch

Partner with product managers to design experiments in Amplitude: define hypotheses, select metrics, set guardrails, and size tests

Build Amplitude charts, funnels, cohorts, and dashboards that help product teams answer their own questions without waiting on you

Debug identity resolution, user properties, and experiment assignment issues across Segment, Amplitude, and downstream tools

Analyze experiment results and present findings to stakeholders with clear recommendations

Build, maintain, and optimize ELT pipelines using dbt, Prefect and Fivetran

Own core datasets and dbt models in Snowflake in partnership with the BI and product engineering teams

Lead infrastructure modernization: orchestration and dbt version upgrades, flow runtime reduction, Snowflake compute and storage cost optimization

Drive warehouse security and governance work, including access controls, remediation of security assessment findings, and PII handling

Implement data quality tests, freshness checks, observability, and alerting across critical pipelines

Troubleshoot pipeline failures and data incidents, and drive root-cause fixes rather than patches

Deliver ready-to-consume data marts that power product features such as recommendations and personalization, plus the baselines to measure them

Contribute to the semantic layer that gives BI tools and AI features a single source of truth for metric definitions

Supply reliable, well-governed data to AI features that improve the member experience and vet workflows

Define and document the company’s core metrics so every team calculates things the same way

6+ years of experience in analytics engineering, data engineering, or hybrid data roles

Strong proficiency in SQL and Python

Deep hands-on experience with Snowflake or a comparable cloud data warehouse

Production experience with dbt, including testing, documentation, and CI

Experience with product analytics platforms, ideally Amplitude, including funnels, cohorts, experiment configuration, and identity resolution

Experience with customer data platforms and event pipelines, ideally Segment, including tracking plan design, destination management, and debugging

Demonstrated experience designing and analyzing A/B tests or product experiments

Experience with orchestration tools such as Prefect, Airflow, or Dagster

Comfortable with Git-based workflows and CI/CD tools such as GitHub Actions

Uses AI tools (code assistants, LLMs, etc.) as part of daily work and can show how

Familiarity with BI tools like Sigma, Looker, or Tableau

Bonus: experience with semantic layers, data contracts, ML/LLM applications in production, or cloud infrastructure (AWS, GCP, or Azure)

You treat trust in the numbers as the product. When a metric is wrong, you feel it personally

You can build an event taxonomy from scratch and also explain to a PM why their experiment needs a bigger sample size

You’re as comfortable writing dbt models as you are reviewing an experiment readout with a product lead

You use AI tools every day to move faster and expect to keep pushing that boundary

You’re self-directed and comfortable managing your own priorities in a fast-moving environment

You care about data quality, reproducibility, and documentation because you’ve been burned by their absence

You love animals

Competitive salary range between $185K - $220K CAD

Hybrid working location

Health, Dental and Vision Insurance

Life and Disability Insurance

Flexible PTO 

Mental Wellbeing Options

Robust Holiday Schedule

Registered Retirement Savings Plan

Growth Opportunities!