AI Analytics Engineer (Marketing Analytics) at Airtable · Syracuse-…

Summary: Airtable is the no-code app platform that empowers organizations to transform how work gets done. They are seeking an Analytics Engineer to join their Data Science & Analytics team, responsible for building data infrastructure and dashboards to support data-driven marketing decisions. Responsibilities: - 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 - 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 - 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 - 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 - 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 Required Qualifications: - 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 Qualifications: - 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) - Full-stack Mindset: You own problems end-to-end and drive the solution, even if it requires expanding the original scope - Bias for Action: You prioritize effective delivery over perfection, operating with a 'ship, learn, and iterate' mentality - Genuinely AI-Native: AI tools are fundamental to your work process. You leverage them to write cleaner SQL, debug models faster, generate documentation, and prototype solutions, and can articulate your specific usage - Data Storyteller: You provide definitive business closure—framing findings as actionable recommendations, not just delivering technically correct output - Thrives in Ambiguity: You proactively create clarity and forward momentum, even when requirements are incomplete or rapidly changing Required Skills: Expert-level SQL, Proficiency with dbt, BI, visualization platforms, Active use of AI tools Important Skills: GitHub for version control, Python for data work, Marketing data concepts, CRM, Databricks Nice-to-Have Skills: Full-stack Mindset, Bias for Action, Data Storyteller, Thrives in Ambiguity Benefits: Benefits, Restricted stock units, Incentive compensation