Job Description

About The Role

The role sits at the intersection of analytics and engineering - translating raw operational data into trustworthy metrics, dashboards, and insights that leadership and product teams use to make decisions weekly.

You will work within a modern data stack alongside data engineers and analytics engineers, owning the analytical layer: semantic models, KPI definitions, and experiments that directly shape product and go-to-market strategy.

Key Responsibilities

Build and maintain core business dashboards in Looker (or Tableau) covering growth, retention, and operational efficiency metrics
Write performant SQL models in Snowflake/BigQuery using dbt, ensuring version control, testing, and documentation for the semantic layer
Design and analyze A/B tests and quasi-experiments, partnering with data scientists on experiment design and statistical rigor
Partner with product, marketing, and finance stakeholders to define KPIs, diagnose metric anomalies, and deliver actionable insights
Develop Python-based analyses (pandas, Jupyter) for churn modeling, cohort analysis, and funnel optimization
Identify and fix data quality issues upstream in ELT pipelines (Airflow/Fivetran), collaborating with data engineers on root cause resolution
Automate recurring reporting workflows, eliminating manual processes and improving self-serve analytics for the organization

What We Are Looking For

3–6 years of experience in data analytics, business intelligence, or analytics engineering, ideally at a high-growth technology company
Expert-level SQL and hands-on experience with dbt or similar transformation frameworks in a cloud warehouse (Snowflake, BigQuery, Redshift)
Strong Python skills for data analysis and automation (pandas, matplotlib or equivalent)
Demonstrated experience building dashboards and semantic models in Looker, Tableau, or Power BI
Solid foundation in statistics: hypothesis testing, experiment design, regression, and common analytical pitfalls
Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
Bonus: experience with data catalog tools (Atlan, Monte Carlo), reverse ETL workflows, or owning metric layers in a data mesh environment

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