Business Data Analyst

Evlo AI; Seattle (WA); On-site; USD 120,000 - 170,000; Full time

Job summary

Evlo AI in Seattle seeks a Business Data Analyst to turn complex pipelines into actionable metrics and dashboards for leadership. You’ll model data with analytics engineers, define KPIs with product, ops, and finance, and quantify initiatives’ impact.

You’ll write efficient SQL, build dashboards in Looker/Tableau/Power BI, and collaborate on dbt models while applying Python or R for analyses and experiments.

Qualifications

• 3–6 years in business analytics or similar at a product-led tech company.
• Expert SQL with window functions and performance tuning.
• Proficiency in Python or R for modeling and automation.
• Experience with Looker/Tableau/Power BI dashboards used by executives.
• Familiarity with dbt, Fivetran, and Airflow for data workflows.
• Bachelor’s degree in a quantitative field or equivalent experience.

Responsibilities

• Build and maintain executive dashboards with metric definitions.
• Write performant SQL against cloud warehouses to answer business questions.
• Collaborate on dbt models and semantic layers for data quality.
• Define and monitor KPI frameworks across product, growth, and ops.
• Conduct deep-dive analyses and present actionable recommendations.
• Run statistical analyses and A/B tests; translate results for leadership.
• Partner with product and finance during planning cycles with forecasts.

Skills

Advanced SQL

Python or R

Analytical thinking

Cross-functional collaboration

Education

Bachelor's degree in a quantitative field

Tools

Looker

Tableau

Power BI

dbt

Fivetran

Airflow

Job description

About The Role

The Business Data Analyst sits at the intersection of data infrastructure and business decision-making, turning complex pipelines and warehouse data into metrics, dashboards, and insights that leadership actually uses to steer the company.

The role works closely with analytics engineers to model data correctly at the source, and directly with product, ops, and finance stakeholders to define KPIs, investigate anomalies, and quantify the impact of business initiatives.

Key Responsibilities

• Build and maintain executive and operational dashboards in Looker (or Tableau), owning metric definitions and ensuring consistency across teams
• Write performant SQL against cloud data warehouses (Snowflake, BigQuery, or Redshift) to answer ambiguous business questions with rigorous, reproducible analysis
• Partner with analytics engineers to develop and maintain dbt models and semantic layers, ensuring data quality and test coverage on core business tables
• Define, implement, and monitor KPI frameworks across product, growth, and operations, including funnel, retention, and cohort analyses
• Run deep-dive investigations — churn drivers, margin shifts, usage anomalies — and deliver clear written and visual recommendations to senior stakeholders
• Perform statistical analyses and A/B test evaluations in Python or R, translating results into business language and actionable next steps
• Serve as the go-to partner for product and finance teams during planning cycles, providing models and forecasts grounded in reliable data

What We Are Looking For

• 3–6 years of experience in business analytics, data analytics, or a comparable role at a product-led technology company
• Expert-level SQL, including window functions, CTEs, and query optimization against large-scale cloud data warehouses
• Hands-on experience with at least one BI platform (Looker, Tableau, or Power BI) and ownership of dashboards used by executives or senior leaders
• Working proficiency in Python or R for analysis, modeling, and automation
• Familiarity with modern data stack tooling (dbt, Fivetran, Airflow) and experience collaborating with analytics or data engineering teams
• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
• Bonus: Experience with experimentation platforms, revenue/margin analytics, LookML development, or building semantic layers; familiarity with Snowflake and dbt in production