AI Data Engineer

We are a tech-enabled growth firm–at the intersection of marketing, consulting & data intelligence–igniting revenue and brand recognition for leading and emerging companies around the world. As a people-first firm, we value diversity in backgrounds and experiences. We strongly believe our people and culture are key to our success. Our vision is to be recognized as the most valued and respected private growth marketing firm in the world–with a scalable brand, culture and services. Our mission is to power the relentless pursuit of growth and redefine what’s possible through a team of growth-obsessed experts who demand innovation and results - driven by integrity, autonomy, and grit.

As a full-service growth marketing firm, we offer best-in-class services including: SEO, Content Marketing, Paid Media, Social Media Marketing, Programmatic + CTV, Public Relations, Influencer Marketing, Email + SMS, Conversion Rate Optimization, Retail Marketing, and Creative. Here at Power Digital, we are hyper-focused on helping brands drive revenue growth and brand recognition, ultimately driving irrefutable value for our clients. 

At the heart of Power Digital is our proprietary technology, nova, which analyzes businesses through first-party data, simplifying investment planning for marketing and diligence in M&A––putting marketers in a strategic seat at the table––and providing value in unparalleled ways. 

Managing billions in media, our dynamic team––of consultative marketers, creatives, analysts and technologists––challenge traditional ways of planning and measurement through meticulous testing and data science across each milestone of the customer journey.

You operate at the intersection of data engineering and applied AI, building end-to-end solutions that power Power Digital’s internal AI platform. Your work goes beyond traditional data pipelines — you take ownership from raw data ingestion through to AI-ready outputs and features. You collaborate closely with data science and product teams, ensuring that data systems directly translate into functional AI capabilities used across the organization.

• →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

• 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

• 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.

• 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