Data Engineer, AI & Analytics

Overview

Data Engineer (AI & Analytics) responsible for building and owning the core data foundation—pipelines, modeling, and semantic layers—that power analytics and AI initiatives.

What you'll do

• Design, build, and maintain ingestion, modeling, and data marts for downstream agency, client, internal, and AI consumers.
• Build ingestion to handle ad-platform API changes, deprecated fields, rate limits, and retroactive conversion restatements.
• Model and reconcile marketing metrics (spend, impressions, conversions, revenue) across multiple ad platforms and customer-level sources.
• Create semantic layers and metric definitions to support consistent, correct AI-generated SQL outputs.
• Use AI-agentic workflows and AI coding tools to accelerate development of data infrastructure.
• Monitor and resolve data quality issues, optimizing pipelines for cost and performance in a multi-client warehouse.

What you'll need

• 3+ years in data or analytics engineering.
• 1+ years owning a dbt project of meaningful size in production.
• Advanced Python and SQL proficiency for production-grade pipeline and modeling code.
• Deep expertise in dbt, including incremental strategies, Jinja/macros, tests, snapshots, and DAG/materialization management.
• Strong Snowflake and surrounding cloud data stack knowledge to operate autonomously.
• Experience modeling in a multi-tenant environment, deciding when logic belongs in core vs client layers.
• Real adoption of AI-agentic development workflows/tools (Cursor, Claude Code, GitHub Copilot).

Details

• Work location: Remote in Spain.