Data Engineer, AI & Analytics
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Job Description
Data Engineer building unified, AI-ready marketing data foundations for Power Digital, an AI-native growth firm. Owning ingestion, dbt modeling, semantic layers, and production pipelines.
Responsibilities:
- Design, build, and maintain the core data foundation, including ingestion, modeling, and data marts
- Build ingestion resilient to API changes, deprecated fields, rate limits, and retroactive conversion restatements
- Model data across Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, Shopify, Klaviyo, GA4, and client CRMs
- Contribute to client-bespoke modeling and client-specific data marts while extending the shared foundation
- Build semantic layers and metric definitions for consistent AI-generated SQL results
- Use AI-agentic workflows and AI coding tools to accelerate development and build intelligent data infrastructure
- Collaborate with nova product and engineering, AI/innovation, Client Service, BI, Tagging & Tracking, Data Ops, and client teams
- Monitor and resolve data quality issues and optimize pipelines for cost and performance across a multi-client warehouse
- Build and ship end-to-end data systems and production-ready datasets and pipelines supporting AI, product, agency, and client teams
- Reduce data fragmentation by building unified, AI-ready data foundations
Requirements:
- Proficiency in spoken and written English at an advanced level is required for this role
- 3+ years in data or analytics engineering, including 1+ years owning a dbt project of meaningful size in production
- Advanced proficiency in Python and SQL, with a focus on production-grade code for data pipelines and modeling
- Deep expertise in 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 modeling in a multi-tenant environment
- Working knowledge of marketing and advertising datasets, including UTMs, attribution windows, and platform-reported versus warehouse-reported conversions
- Proven experience designing and managing end-to-end data lifecycles from ingestion to serving
- Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles
- Real adoption of AI-agentic development workflows, including Cursor, Claude Code, or GitHub Copilot
- Demonstrated ability to architect AI-ready data models, including feature stores and semantic layers
- Experience with Git and CI/CD best practices, including automated testing
- Comfortable shipping iteratively and refining data products based on live feedback
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