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Senior Analytics Engineer II

Wrapbook LogoWrapbook
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Salary

CA$148,000 - CA$203,000

Location

Remote - US & Canada

Posted

Today

We’re looking for a Senior Analytics Engineer II to evolve and own Wrapbook’s analytics layer and tech stack.

In this role, you’ll build and scale the data foundations that enable fast, reliable, and actionable insights across Wrapbook. You’ll partner with business stakeholders and Engineering on end-to-end analytics work, from source systems and data models to governed metrics, self-serve analytics, and production monitoring.

You’ll make our analytics foundation usable by both people and AI agents through clear documentation, governed data, and strong data quality controls. You’ll also set standards for how analytics work is built, tested, monitored, and maintained.

If you enjoy solving complex data problems, can navigate ambiguity, proactively identify solutions, and are excited to rethink how teams and AI work with data, this role is for you

What you'll do

  • Lead the development of analytics products and self-serve tools so teams across Wrapbook can access trusted data and use it independently.
  • Own analytics products end to end, from defining success metrics and designing schemas through testing, production deployment, data quality, freshness SLAs, and incident resolution.
  • Build and maintain reliable ETL/ELT pipelines and canonical datasets that support analytical workflows across the company.
  • Build the governed data layer, including semantic models, canonical views, metric definitions, and data contracts. Maintain versioning and backward compatibility so downstream BI and AI consumers remain stable.
  • Structure models, definitions, and documentation so data is usable by people and by AI agents. Build internal tools that help teams ask data questions and trust the answers they receive.
  • Identify business questions, translate them into structured analytical problems, and develop data solutions that answer them.
  • Mentor the Analytics team by setting standards for testing, dbt CI/CD, monitoring, alerting, automation, runbooks, onboarding, and code review.
  • Partner with Engineering to understand source schemas, events, and system architecture. Provide input early so data entering the warehouse is reliable, stable, and easy to model.

What you'll have

  • 4+ years in data or analytics engineering. You've built and operated production pipelines end to end and owned issues when they broke.
  • Strong SQL and solid Python. Hands-on experience with ETL and orchestration tools (Airbyte, Fivetran, Dagster, dbt, Airflow, or similar), including multi-step workflows.
  • Experience with modern data warehouses (Snowflake, Databricks, or Redshift), including query and performance optimization.
  • Strong data modeling skills. You understand the business meaning behind the metrics, not just how the data moves.
  • A bias for action. You ship useful, iterative data models that deliver value without waiting for a perfect solution.
  • Experience building governed data layers (semantic models, canonical views, metric definitions, or data contracts).
  • Experience writing data quality checks to validate data integrity (dbt tests, Great Expectations, Pydeequ, or similar).
  • Experience with BI tools (Omni, Mode, Looker, Tableau, or similar). You've delivered reports and dashboards.
  • Experience setting standards for dbt CI/CD, monitoring, alerting, runbooks, and code review, and mentoring others on those practices.
  • A track record of evaluating and adopting new data and analytical tools when they solve a real problem or improve the stack.

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