Data Engineer, AI & Analytics
Power Digital2 months ago
Remote, MexicoMid Level
Responsibilities
- Design, build, and maintain ingestion pipelines, data models, data marts, and serving layers for agency, client, internal, and AI consumers.
- Handle advertising platform API changes, deprecated fields, rate limits, and retroactive conversion restatements without corrupting downstream models.
- Reconcile spend, impressions, conversions, revenue, and customer-level data across advertising, commerce, analytics, and CRM sources.
- Build client-specific models and marts while determining which logic belongs in the shared core versus a client layer.
- Build semantic layers and metric definitions that support consistent AI-generated SQL results.
- Use AI-agentic coding workflows to accelerate development and document reusable team patterns.
- Collaborate with product, engineering, AI and innovation, client service, BI, tagging and tracking, and data operations teams.
- Monitor data quality, resolve pipeline issues, and optimize warehouse pipelines for cost and performance.
- Deliver reliable data assets supporting BI, AI, product, and client-facing applications.
Requirements
- At least 3 years of experience in data or analytics engineering, including at least 1 year owning a meaningful dbt project in production.
- Advanced proficiency in Python and SQL for production-grade pipelines and data modeling.
- Deep expertise with dbt, including incremental strategies, full-refresh tradeoffs, Jinja, macros, packages, tests, snapshots, source freshness, exposures, DAG management, and materializations.
- Strong command of Snowflake and the surrounding cloud data stack.
- Experience with multi-tenant data modeling and end-to-end data lifecycles from ingestion through serving.
- Working knowledge of marketing and advertising datasets, including UTMs, attribution windows, and platform-versus-warehouse conversion differences.
- Familiarity with GCP and infrastructure-as-code principles.
- Hands-on adoption of AI-agentic development tools such as Cursor, Claude Code, or GitHub Copilot.
- Experience architecting AI-ready data models, including feature stores and semantic layers.
- Experience with Git, automated testing, and CI/CD best practices.
- Ability to iterate on 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, retail, or marketplace 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.