Data Analyst, Marketing Funnel for Clean Energy (Remote)

Havenenergy; United States; Remote; USD 1,800 - 3,600; Part time

Job summary

Haven Energy is seeking a contract analyst to perform hands-on analytics across the business, with emphasis on marketing and sales funnel optimization. You’ll map leads from first touch through qualification, sale and install, define counting rules, and translate findings into actionable insights for non-technical stakeholders.

The role relies on Snowflake, Fivetran and dbt, leverages AI tooling for routine work, and requires you to communicate clearly while delivering reproducible models.

Qualifications

• 4+ years in analytics, data analysis, or a hybrid data role.
• Deep experience with marketing and sales funnels and attribution tradeoffs.
• Hands-on SQL skills to derive answers from data.
• Comfort with a modern data stack (dbt, Git).
• Ability to scope work and explain data to non-technical audiences.
• Experience with CRM data, especially HubSpot.
• Experience using AI/LLM tooling for analytics.

Responsibilities

• Analyze the conversion funnel from first touch to install.
• Write documentation on current implementations and new analytics approaches.
• Analyze conversion rates by channel, campaign, geography, program, and customer segment.
• Evaluate CAC and payback by channel within a multi-step sales cycle.
• Build and maintain dashboards for the team; present findings to stakeholders.

Skills

SQL

Marketing analytics

Funnel modeling

Data visualization

Communication

Tools

Snowflake

Fivetran

dbt

Git

Job description

Haven Energy is seeking a contract analyst to perform hands-on analytics across the business, with emphasis on marketing and sales funnel optimization. You’ll map leads from first touch through qualification, sale and install, define counting rules, and translate findings into actionable insights for non-technical stakeholders.

The role relies on Snowflake, Fivetran and dbt, leverages AI tooling for routine work, and requires you to communicate clearly while delivering reproducible models.