Power Digital Marketing
AI Data Engineer
Rol remoto de AI & Data Engineering con fit claro de ubicación del candidato.
Publicado5 jul 2026
Países elegibles4 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
ArgentinaBrasilColombiaMéxico
Resumen del rol
AI Data Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Responsibilities:
- Build and own end-to-end data pipelines in Snowflake — from raw ingestion through transformation to serving layers for AI products
- Partner with ML engineers and data scientists to build and maintain AI-specific data infrastructure
- Consolidate fragmented data sources across the organization into reliable, automated pipelines
- Design scalable data models and marts that serve both analytics and ML feature engineering
- Support rapid iteration on new data products and features in a fast-moving environment
- Collaborate cross-functionally with analytics, data science, and product teams to translate requirements into data solutions
- Proactively monitor and resolve data quality issues, optimizing for cost and performance
- Employ AI technologies to enhance and optimize business processes
- Utilize and leverage Power Digital's Nova ecosystem as it relates to your department
- Use AI coding tools as part of your daily development workflow to accelerate pipeline development and data quality work
Role Requirements:
- Strong proficiency in Python and SQL
- Experience building end-to-end data solutions, from ingestion to production use
- Experience with Snowflake or a similar cloud data warehouse
- Working knowledge of AWS (e.g., S3, Lambda, EventBridge)
- Understanding of data modeling and structuring data for downstream applications
- Familiarity with Git and basic CI/CD practices
- Active use of AI tools in development workflows (e.g., Claude Code, Cursor, Copilot)
- Comfortable shipping fast and iterating from live user feedback
- Hands-on exposure to AI workflows (e.g., embeddings, vector databases, RAG systems)
- Looker or similar BI/semantic layer experience
Key Performance Indicators (KPIs)
- Pipeline Reliability: ≥95% weekly uptime on all production pipelines owned (Signal, GTM, Churn, SE, Iris data feeds) by end of Month 3, measured via Snowflake/Render monitoring. Any P1 failure acknowledged within 4 hours with a status update.
- Data Quality: ≥90% row-level completeness on serving tables (starting with final_features) by end of Month 2, with at least one automated quality check running in production.
- Shipping Velocity: At least 1 data improvement shipped to production per week by the end of Month 2. This includes new data sources connected, pipeline fixes, Looker model additions, quality patches, or latency improvements. Measured by production deploys. The bar is shipping, not scoping.
- Feature Contribution (Month 4+): At least 1 AI product (Churn, SE, or Iris) has a measurable data improvement — new source added, latency reduced, or accuracy lift — directly attributable to this engineer's work before end of Q2.
Most Important Things (MITs)
- Build and ship end-to-end data systems that directly enable AI features
- Deliver production-ready datasets and pipelines that unblock AI and product teams
- Reduce fragmentation by creating unified, AI-ready data foundations
Benefits & Perks:
- Base salary paid in USD
- Unlimited Time Off available on day one
- Fully flex work environment: full-remote, in-office, or hybrid
- Healthcare stipend: $50/month to support your healthcare or insurance costs
- National Holidays + 2 Mental Health Recharge Days per year
- Unlimited opportunities for growth & leadership within a rapidly growing firm
- Ongoing employee development programs for personal and professional growth (Hedgehog and Vital 5s)
- Quarterly awards including prize money and recognition for outstanding performance
- Opportunities to be involved in company DEI initiatives
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