Business Data Analyst

Evlo AI; Denver (CO); Vor Ort; USD 100.000 - 160.000; Vollzeit

Zusammenfassung

Evlo AI is seeking an analytics engineer to turn raw data into decision-grade dashboards and insights. You will own the semantic layer, define metrics, and collaborate with product, growth, and finance to answer questions the warehouse cannot yet address.

You will build data models in Snowflake with dbt, ship executive dashboards, and work with data engineers to improve data quality and reliability. A strong statistical mindset and SQL/Python fluency are essential.

Qualifikationen

• 3–6 years of analytics or analytics engineering experience.
• Expert-level SQL with complex window functions, CTEs, and large warehouses.
• Hands-on dbt experience or equivalent transformation framework.
• Strong dashboarding skills with Looker/Tableau/Power BI and semantic models.
• Proficiency in Python (pandas) for ad hoc analysis and automation.
• Bachelor’s degree in a quantitative field or equivalent practical experience.
• Bonus: experience with experimentation platforms, event analytics tools, or reverse ETL tools.

Aufgaben

• Build and maintain curated data models in Snowflake using dbt.
• Design and ship dashboards in Looker (or Tableau) for executives and teams.
• Partner with data engineers to improve pipelines, data quality, and SLA monitoring.
• Run deep-dive analyses on retention, funnel, unit economics, and experiments with SQL and Python.
• Translate business questions into rigorous analyses with clear methodology and recommendations.
• Define and document the metrics catalog across the organization.
• Support A/B test design and evaluation with product and engineering teams.

Kenntnisse

SQL proficiency

Analytical mindset

Experimentation & stats

Stakeholder communication

Ausbildung

Bachelor's degree in quantitative field

Tools

Snowflake

BigQuery

Redshift

dbt

Looker

Tableau

Power BI

Python (pandas)

Jobbeschreibung

About The Role

The role sits at the intersection of data engineering and business analytics - turning raw pipeline data into decision-grade metrics, dashboards, and analyses that leadership and product teams actually use.

About The Role

The role sits at the intersection of data engineering and business analytics - turning raw pipeline data into decision-grade metrics, dashboards, and analyses that leadership and product teams actually use.

The position owns the semantic layer of a growing data platform: defining metrics, maintaining dbt models, and partnering with stakeholders across product, growth, and finance to answer questions the warehouse can't answer yet.

Key Responsibilities

• Build and maintain curated data models in Snowflake using dbt, ensuring consistent metric definitions across BI tools and downstream consumers
• Design and ship dashboards in Looker (or Tableau) for executives, product managers, and marketing teams - with clear documentation, not one-off reports
• Partner with data engineers to improve pipeline reliability and data quality; define tests, monitor freshness, and triage broken SLAs
• Run deep-dive analyses on retention, funnel conversion, unit economics, and experiment results using SQL and Python
• Translate ambiguous business questions into rigorous analyses with clear methodology, quantified confidence, and actionable recommendations
• Define and document the metrics catalog, acting as the arbiter of 'what does this number actually mean' across the organization
• Support A/B test design and evaluation, working with product and engineering teams on experiment readouts

What We Are Looking For

• 3-6 years of experience in analytics, business intelligence, or analytics engineering at a product-led or data-driven company
• Expert-level SQL - complex window functions, CTEs, query optimization - and comfort working directly in large warehouses (Snowflake, BigQuery, or Redshift)
• Hands-on experience with dbt (models, tests, documentation) or an equivalent transformation framework
• Strong dashboarding skills in Looker, Tableau, or Power BI, including experience building the semantic models behind them
• Working knowledge of Python (pandas) for ad hoc analysis and automation
• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
• Bonus: Experience with experimentation platforms and statistical testing (t-tests, Bayesian methods), event analytics tools (Amplitude, Mixpanel), or exposure to reverse ETL and data activation tools (Hightouch, Census)