Data Analyst

Evlo AI; San Francisco (CA); In loco; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Evlo AI in San Francisco seeks a data analytics engineer who translates product and business data into metrics, dashboards, and analyses that drive company-wide decisions.

You will partner with product, growth, and finance, own data quality, define metrics, and help evolve the data warehouse using Snowflake, BigQuery, Redshift, dbt, and BI tools. This role emphasizes end-to-end analyses, clear storytelling, and scalable data infrastructure.

Competenze

• 3–6 years in data analytics or analytics engineering at a product-driven tech company.
• Advanced SQL (window functions, CTEs) and Python or R for analysis.
• Hands-on dbt with Snowflake/BigQuery/Redshift.
• Experience building executive dashboards in Looker/Tableau/Mode.
• End-to-end analytics ownership and decision influence.
• Bachelor's in a quantitative field or equivalent experience.
• Bonus: Amplitude, Mixpanel, A/B testing, or reverse ETL tools.

Mansioni

• Build and maintain core dashboards and self-serve reporting in Looker, Tableau, or Mode.
• Write performant SQL and dbt models to transform raw event and application data into trusted, documented marts.
• Analyze retention, activation, funnel, and unit economics; deliver clear recommendations.
• Collaborate with data engineers to evolve the warehouse architecture (Snowflake, BigQuery, Redshift) and testing/orchestration.
• Define product metrics with engineering; ensure reliable event tracking.
• Own data quality monitoring; build tests and anomaly alerts.
• Translate ambiguous business questions into analytical frameworks; present findings visually.

Conoscenze

Advanced SQL

Python or R

Executive dashboards

Data visualization

End-to-end analytics

Formazione

Bachelor's degree in quantitative field

Strumenti

Snowflake

BigQuery

Redshift

dbt

Airflow

Dagster

Looker

Tableau

Mode

Descrizione del lavoro

About The Role

The role sits at the intersection of analytics and data infrastructure, translating raw product and business data into metrics, dashboards, and analyses that drive company-wide decisions.

You will partner directly with product, growth, and finance teams while contributing to the shared data model — defining metrics, owning data quality, and ensuring the warehouse reflects how the business actually operates.

Key Responsibilities

• Build and maintain core dashboards and self-serve reporting in Looker, Tableau, or equivalent BI tools used daily by product and go-to-market teams
• Write performant SQL and dbt models to transform raw event and application data into trusted, documented, version-controlled marts
• Design and run analyses on retention, activation, funnel conversion, and unit economics, delivering clear recommendations to stakeholders
• Partner with data engineers to evolve the warehouse architecture (Snowflake, BigQuery, or Redshift), including testing, orchestration via Airflow or Dagster, and data contracts
• Define and instrument product metrics in collaboration with engineering, ensuring event tracking is complete, consistent, and reliable
• Own data quality monitoring — building tests, anomaly alerts, and root‑cause processes that keep metrics trustworthy
• Translate ambiguous business questions into structured analytical frameworks, and communicate findings through clear visualizations and narratives

What We Are Looking For

• 3–6 years of experience in data analytics, analytics engineering, or a closely related role at a product-driven technology company
• Advanced SQL skills — window functions, CTEs, query optimization — and proficiency in Python or R for analysis
• Hands‑on experience with dbt and modern warehouse tooling (Snowflake, BigQuery, or Redshift)
• Experience building executive‑facing dashboards in Looker, Tableau, or Mode, with strong data visualization judgment
• Track record of owning analyses end-to-end and influencing decisions through them, not just producing reports
• Bachelor's degree in a quantitative field (statistics, economics, CS, engineering) or equivalent practical experience
• Bonus: experience with product analytics tools (Amplitude, Mixpanel), experimentation/A-B testing frameworks, or reverse ETL tools like Hightouch and Census