Responsibilities

• Canonical Marketing Data Sources
• Design and maintain trustworthy data models for core marketing metrics, managing the full lifecycle from prototyping through production.
• Develop and govern dbt data pipelines, establishing data integrity standards and SLAs for timely, accurate delivery across the Marketing organization.
• Critical Dashboards and Self-Serve Tooling
• Build and optimize dashboards that deliver real-time, self-serve insights across high-priority marketing areas: campaign performance, funnel conversion, pipeline contribution, and lead scoring.
• Drive data independence for Marketing stakeholders, eliminating reliance on ad-hoc data requests and manual reporting.
• AI-Native Data Infrastructure
• Collaborate with the Marketing team and data partners to establish the AI Business Context layer for marketing use cases.
• Lead the development of tools that facilitate natural language data access and AI-assisted reporting for non-technical stakeholders.
• Trusted Partnership
• Serve as the primary data partner for marketing managers, demand generation teams, and leadership.
• Translate complex data insights into clear business recommendations via dashboards, memos, and presentations.
• Domain Expertise
• Achieve a comprehensive mastery of Airtable's marketing data models, existing pipelines, and BI tools (dbt / Looker / Omni) within the first 6 months, becoming the definitive internal expert.

Requirements

• Expert-level SQL: Proven ability to write complex queries involving joins, aggregations, and window functions.
• Proficiency with dbt or equivalent data transformation tools.
• Experience with BI and visualization platforms (Looker, Omni, Tableau, Hex, or similar).
• Active, demonstrated daily use of AI coding tools (Cursor, Claude, ChatGPT, Gemini). Candidates must provide specific, concrete examples of how these tools are integral to their work, moving beyond simple familiarity.
• Mandatory use of GitHub for version control in a standard development workflow.
• Exceptional communication skills: the ability to translate technical data findings into compelling business narratives for non-technical leadership.

Preferred

• Python for data work (pandas, ETL scripting, or analysis).
• Prior exposure to marketing data concepts: attribution, funnel metrics, lead scoring, or campaign performance.
• Familiarity with CRM (Salesforce) or marketing automation platforms (Marketo).
• Experience with Databricks or cloud data warehouses.
• A public portfolio showcasing data or AI-assisted engineering work (GitHub, personal projects, Kaggle).

Benefits

• Our total compensation package also includes the opportunity to receive benefits, restricted stock units, and may include incentive compensation.