Data Engineer, AI & Analytics
Power Digital2 months ago
Remote, SpainMid Level
Responsibilities
- Design, build, and maintain ingestion pipelines, data models, data marts, semantic layers, and serving layers for agency, client, internal, product, and AI consumers.
- Handle advertising-platform API changes, deprecated fields, rate limits, and retroactive conversion restatements without corrupting downstream models.
- Model and reconcile spend, impressions, conversions, and revenue across Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft, with customer-level joins across Shopify, Klaviyo, GA4, and client CRMs.
- Build recurring client-specific models, overrides, and marts while determining which reusable logic belongs in the shared core.
- Define semantic layers and metrics that support consistent AI-generated SQL results.
- Use AI-agentic coding workflows to accelerate development, document effective patterns, and build intelligent data infrastructure.
- Collaborate with product, engineering, AI/innovation, client, BI, tagging and tracking, and data operations teams to translate requirements into data solutions.
- Monitor and resolve data quality issues and optimize multi-client pipelines for cost, performance, reliability, and downstream adoption.
Requirements
- At least 3 years of data or analytics engineering experience, including at least 1 year owning a meaningful dbt project in production.
- Advanced Python and SQL proficiency for production-grade pipeline and modeling work.
- Deep dbt expertise, including incremental strategies, full-refresh tradeoffs, Jinja, macros, packages, tests, snapshots, source freshness, exposures, DAG management, and materializations.
- Strong Snowflake expertise and the ability to operate autonomously as a foundational data owner.
- Experience modeling in multi-tenant environments and deciding whether client-specific logic belongs in the shared core or client layer.
- Working knowledge of marketing and advertising data, including UTMs, attribution windows, and platform-versus-warehouse conversion differences.
- Experience designing and managing end-to-end data lifecycles from ingestion through serving for BI and AI applications.
- Familiarity with GCP and infrastructure-as-code principles.
- Hands-on adoption of Cursor, Claude Code, or GitHub Copilot for coding, debugging, and architecture.
- Experience architecting AI-ready data models, including feature stores and semantic layers.
- Experience with Git, automated testing, and CI/CD best practices.
- Ability to ship iteratively and refine data products using live feedback.
- Advanced spoken and written English proficiency is required.
- Agency, consultancy, services, measurement, incrementality, media mix modeling, attribution, server-side tagging, or retail and marketplace data experience is helpful but not required.
Tech Stack
Categories
Data Engineering
About Power Digital
Power Digital is a privately held marketing services firm that provides SEO, PPC, social, creative, web development, and conversion rate optimization to consumer and ecommerce brands. Founded in 2012 and headquartered in San Diego, it augments client work with nova, a proprietary platform that analyzes first-party data to inform channel and budget decisions. Engagements are typically retainer- or project-based.