Data Engineer, AI & Analytics

Role overview Own the core data foundation for an AI-native growth agency, building pipelines, models, and data marts that serve internal teams, clients, and AI initiatives. The work spans raw platform ingestion through a semantic layer across a large multi-tenant warehouse, with AI-agentic workflows embedded into how you build. Marketing data is fragmented by design, so the role centers on reconciling spend, conversions, and revenue across many platforms.

Responsibilities - Design, build, and maintain the core data foundation, owning the workflow from raw platform data through serving layers. - Build ingestion that tolerates API changes, deprecated fields, rate limits, and retroactive restatement of conversion data without corrupting downstream models. - Model across sources to reconcile spend, impressions, conversions, and revenue across major ad platforms, plus customer-level joins across e-commerce, email, analytics, and CRM systems. - Deliver client-bespoke modeling on top of the core layer, extending shared patterns rather than forking them. - Build semantic layers and metric definitions that let AI-generated SQL return consistent, correct answers. - Use AI-agentic workflows, including AI coding tools, to accelerate development and document patterns that become team standards. - Monitor and resolve data quality issues, optimizing pipelines for cost and performance across a multi-client warehouse.

Requirements - 3+ years in data or analytics engineering, including 1+ years owning a dbt project of meaningful size in production. - Advanced proficiency in Python and SQL, with a focus on production-grade code for data pipelines and modeling. - Deep dbt expertise: incremental strategies, Jinja and macros, packages, tests, snapshots, source freshness, exposures, and DAG hygiene. - Strong command of Snowflake and the surrounding cloud data stack to operate autonomously. - Experience modeling in a multi-tenant environment, with judgment about what belongs in a client layer versus the core. - Working knowledge of marketing and advertising datasets, including UTMs, attribution windows, and the gap between platform-reported and warehouse-reported conversions. - Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles. - Practical adoption of AI-agentic development workflows such as Cursor, Claude Code, or GitHub Copilot.

Nice to have - Experience serving both BI and AI consumers from the same data foundation.

Benefits and work setup - Pacific time zone working hours expected. - Advanced spoken and written English required for daily collaboration.