Data Engineer, AI & Analytics

Role Overview

As a Data Engineer, you’ll work on the Data Team, owning the core data foundation for Power Digital, including pipelines, modeling, and data marts that power the agency, clients, and AI initiatives. You’ll work end-to-end, from raw ingestion through the semantic layer, using AI-agentic workflows as a normal part of how you build. The data itself is the interesting part, with marketing data being fragmented by default, and every ad platform having its own API, schema, and definition of a conversion.

What You Will Do

Your key responsibilities will include designing, building, and maintaining the core data foundation, building ingestion that handles API changes, deprecated fields, and retroactive restatement of conversion data, modeling across sources so the numbers reconcile, and contributing to client-bespoke modeling on top of the core layer.

Why It Might Be a Fit

You might be a fit if you’re a data engineer at a brand or retailer who wants to work closer to the decisions your data drives, or if you’ve built marketing pipelines at an agency or martech company and know how they break.

Requirements

• 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 and full-refresh tradeoffs, Jinja and macros, packages, generic and singular tests, snapshots, source freshness, exposures, and how to keep a large project’s DAG and materializations under control
• Strong command of Snowflake and the surrounding cloud data stack to operate autonomously as a foundational data owner
• Experience modeling in a multi-tenant environment, with the judgment to know when a client request belongs in a client layer and when it belongs in the core
• Working knowledge of marketing and advertising datasets, including UTMs, attribution windows, and the gap between platform-reported and warehouse-reported conversions
• Proven experience designing and managing end-to-end data lifecycles from ingestion to serving, with reliability that holds for both BI and AI applications
• Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles
• Real adoption of AI-agentic development workflows (Cursor, Claude Code, GitHub Copilot) for coding, debugging, and system architecture
• Demonstrated ability to architect AI-ready data models (feature stores, clean semantic layers) that support downstream initiatives
• Experience with Git and CI/CD best practices, including automated testing you trust
• Comfortable shipping iteratively and refining data products based on live feedback

Benefits

• Competitive salary
• Equity
• Health insurance
• 401(k) plan
• Paid time off
• Flexible work arrangements
• Professional development opportunities