Roofr

Data Analytics Manager

Roofr

full-time

Posted on:

Origin:  • 🇨🇦 Canada

Visit company website
✨ AI Apply
Manual Apply

Job Level

Mid-LevelSenior

Tech Stack

AirflowAmazon RedshiftBigQueryCloudPythonSQL

About the role

  • Lead, mentor, and grow a small, multidisciplinary data team (data engineers, analysts, data scientists).
  • Define, refine and implement best practices for data modeling, transformation (DBT), orchestration (Airflow), and testing.
  • Own the data stack and architecture (e.g., Snowflake, DBT, Metabase/Sigma, Stitch).
  • Collaborate with stakeholders to define KPIs, build dashboards, and provide strategic insights.
  • Drive data governance, quality, documentation, and metric standardization.
  • Guide the development of data products and experimentation frameworks (A/B testing, churn prediction, etc.).
  • Partner with leadership to align data strategy with business objectives.
  • Stay up to date on modern data tooling and recommend improvements to scale infrastructure and processes.

Requirements

  • 5+ years of experience in data roles, with at least 1–2 years managing a technical team.
  • Strong technical skills in SQL, DBT, and orchestration tools like Airflow.
  • Experience with modern data stacks (e.g., Snowflake, Redshift, BigQuery) and ELT tools (Stitch, Fivetran, Airbyte).
  • Proficiency in data visualization and BI tools (Metabase, Sigma, Looker, or similar).
  • Familiarity with Python or R for scripting and data science workflows.
  • Solid understanding of experimentation, forecasting, and statistical analysis.
  • Proven ability to drive cross-functional alignment and communicate clearly with technical and non-technical stakeholders.
  • Passionate about building clean, scalable data infrastructure and enabling data-driven decisions.
  • Experience in a startup or high-growth environment
  • Exposure to customer or product analytics
  • Experience designing data team org structures and hiring
  • Knowledge of dbt Cloud and CI/CD for analytics engineering
  • Curious: You ask “why” often and dig deep to find the real story behind the data.
  • Wise: You balance speed and scale, knowing when “good enough” beats perfect.
  • Technical: You’re comfortable rolling up your sleeves, writing/reviewing a DBT model or debugging a DAG.
  • Collaborative: You work well across teams and translate data into action.