Power Digital is an AI-Native Growth Firm built at the intersection of world-class talent and proprietary technology. We help leading and emerging brands grow by combining deep vertical expertise, strategic thinking, and enterprise intelligence to drive measurable business outcomes. Power Digital is an AI-Native Growth Firm built at the intersection of world-class talent and proprietary technology. We help leading and emerging brands grow by combining deep vertical expertise, strategic thinking, and enterprise intelligence to drive measurable business outcomes. For more than a decade, we have built our business around one belief: the best technology makes great people better. Our teams of strategists, creatives, analysts, and technologists use Omega, our AI operating system, to surface opportunities faster, investigate what is driving performance, and execute approved work with greater speed and precision. Behind Omega is Iris, our proprietary intelligence layer, built on years of real performance data, strategic frameworks, testing, and learnings from across our client portfolio. At Power Digital, we believe the future belongs to teams that combine the best talent with the best technology. Proficiency in spoken and written English at an advanced level is required for this role. You'll sit on the Data Team, which owns the core data foundation for Power Digital: the pipelines, modeling, and data marts that power our agency teams, clients, and AI initiatives. The data itself is the interesting part. Marketing data is fragmented by default. Entity hierarchies don't match (campaign/ad set/ad on Meta, campaign/ad group/ad on Google). You'll work closely with Client Service, BI, Tagging & Tracking, Data Ops, and the nova product/engineering teams. Design, build,



and maintain the core data foundation (ingestion, modeling, and data marts), owning the workflow from raw platform data through the serving layers that support agency, client, internal, and AI consumers. Build ingestion that handles what ad platforms actually do: API changes, deprecated fields, aggressive rate limits, and retroactive restatement of conversion data, all without corrupting downstream models. Model across sources so the numbers reconcile: spend, impressions, conversions, and revenue across Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft, plus customer-level joins across Shopify, Klaviyo, GA4, and client CRMs. Contribute to client-bespoke modeling on top of the core layer: custom logic, overrides, and client-specific marts built in response to individual client requests, with patterns that extend the shared foundation rather than fork it. Build the 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 build intelligent data infrastructure. Document what works so it becomes a standard team pattern. Collaborate cross-functionally with nova (product and engineering), AI/innovation, and client teams to translate requirements into data solutions and support rapid iteration on new products and features. Monitor and resolve data quality issues,



and optimize pipelines for cost and performance across a multi-client warehouse. 3+ years in data or analytics engineering, including 1+ years owning a dbt project of meaningful size in production, not just contributing models to one. Advanced proficiency in Python and SQL, with a focus on production-grade code for data pipelines and modeling. Deep expertise in dbt, not just writing models. Incremental strategies and full-refresh tradeoffs, Jinja and macros, packages, generic and singular tests, snapshots, source freshness, exposures, and how to keep a large project's DAG and materializations under control. Strong command of Snowflake and the surrounding cloud data stack to operate autonomously as a foundational data owner. Experience modeling in a multi-tenant environment, with the judgment to know when a client request belongs in a client layer and when it belongs in the core. You've handled UTMs, attribution windows, and the gap between platform-reported and warehouse-reported conversions. Proven experience designing and managing end-to-end data lifecycles from ingestion to serving, with reliability that holds for both BI and AI applications. Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles. Real adoption of AI-agentic development workflows (Cursor, Claude Code, GitHub Copilot) for coding, debugging, and system architecture. Demonstrated ability to architect AI-ready data models (feature stores, clean semantic layers) that support downstream initiatives. Experience with Git and CI/CD best practices, including automated testing you trust. Comfortable shipping iteratively and refining data products based on live feedback. Server-side tagging, or retail and marketplace data. You're a data en

📌 Data Engineer Sql (España)
🏢 Power Digital Marketing
📍 España