Business Data Analyst

Evlo AI; Austin (TX); On-site; USD 90,000 - 150,000; Full time

Job summary

Evlo AI in Austin, TX seeks a data analytics engineer bridging data engineering and business strategy to turn raw data into reliable models, dashboards, and analyses used across product, finance, and go-to-market teams.

You will own the full path from stakeholder question to production-grade answer, collaborating with data engineers on Snowflake, dbt, and Airflow while building SQL models, dashboards, and KPI definitions.

Qualifications

• 3-6 years of experience in data analytics or analytics engineering.
• Expert SQL skills and experience building transformation logic in dbt or comparable frameworks.
• Proficiency in Python for analysis (pandas) and comfort with statistical concepts.
• Hands-on experience with a modern BI tool: Tableau, Looker, or Mode, including semantic modeling.

Responsibilities

• Design and maintain advanced SQL models in dbt, ensuring data correctness, documentation, and freshness standards across the warehouse.
• Build and own Tableau/Looker dashboards tracking core business metrics - retention, revenue, funnel conversion - used weekly by leadership.
• Partner with product and finance stakeholders to define metric definitions, KPIs, and experiment readouts; translate ambiguous questions into analytical frameworks.
• Run cohort, retention, and A/B test analyses in Python (pandas, numpy), delivering clear, statistically sound recommendations.
• Investigate data quality issues upstream in ELT pipelines, working with data engineers on root-cause fixes and automated tests in dbt.
• Contribute to the semantic layer and metric standards, reducing ad-hoc query duplication across teams.
• Present findings and recommendations to senior stakeholders in written and verbal formats, including weekly business reviews.

Skills

SQL

Python (pandas)

Tableau

Looker

dbt

Statistics

Education

Bachelor's degree in quantitative field

Tools

Snowflake

Airflow

BigQuery

Redshift

dbt

Job description

About The Role

The role sits at the intersection of data engineering and business strategy - transforming raw operational data into reliable models, dashboards, and analyses that drive decisions across product, finance, and go-to-market teams.

About The Role

The role sits at the intersection of data engineering and business strategy - transforming raw operational data into reliable models, dashboards, and analyses that drive decisions across product, finance, and go-to-market teams.

You will work with data engineers and analytics engineers on a modern warehouse stack (Snowflake, dbt, Airflow), owning the full path from stakeholder question to production-grade answer.

Key Responsibilities

• Design and maintain advanced SQL models in dbt, ensuring data correctness, documentation, and freshness standards across the warehouse
• Build and own Tableau/Looker dashboards tracking core business metrics - retention, revenue, funnel conversion - used weekly by leadership
• Partner with product and finance stakeholders to define metric definitions, KPIs, and experiment readouts; translate ambiguous questions into analytical frameworks
• Run cohort, retention, and A/B test analyses in Python (pandas, numpy), delivering clear, statistically sound recommendations
• Investigate data quality issues upstream in ELT pipelines, working with data engineers on root-cause fixes and automated tests in dbt
• Contribute to the semantic layer and metric standards, reducing ad-hoc query duplication across teams
• Present findings and recommendations to senior stakeholders in written and verbal formats, including weekly business reviews

What We Are Looking For

• 3-6 years of experience in data analytics, business intelligence, or analytics engineering
• Expert SQL skills and demonstrated experience building transformation logic in dbt or comparable frameworks
• Proficiency in Python for analysis (pandas) and comfort with statistical concepts: significance testing, confidence intervals, regression
• Hands-on experience with a modern BI tool: Tableau, Looker, or Mode, including semantic modeling
• Experience working with cloud data warehouses (Snowflake, BigQuery, or Redshift) and orchestrators (Airflow or Dagster)
• Bachelor's degree in a quantitative field (statistics, economics, CS, engineering) or equivalent practical experience
• Bonus: experience with experimentation platforms (Statsig, Eppo), revenue analytics or fintech domains, and LightGBM/ML-lite use cases