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 our agency teams, 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, owning the workflow from raw platform data through the serving layers that support agency, client, internal, and AI consumers. You’ll build ingestion that handles API changes, deprecated fields, aggressive rate limits, and retroactive restatement of conversion data, model across sources to reconcile spend, impressions, conversions, and revenue, and contribute 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. You’ll be a good fit if you’re an analytics engineer who wants to own the full system rather than just the modeling layer, or if you’ve rebuilt your own workflow around AI coding agents and want that to be the job.

Requirements

• 3+ years in data or analytics engineering
• 1+ years owning a dbt project of meaningful size in production
• Advanced proficiency in Python and SQL
• Deep expertise in dbt
• Strong command of Snowflake and the surrounding cloud data stack
• Experience modeling in a multi-tenant environment
• Working knowledge of marketing and advertising datasets
• Proven experience designing and managing end-to-end data lifecycles
• Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles
• Real adoption of AI-agentic development workflows
• Demonstrated ability to architect AI-ready data models
• Experience with Git and CI/CD best practices

Benefits

• Competitive salary
• Equity
• Health insurance
• Paid time off
• Retirement plan
• Learning budget
• Parental leave
• Wellness programs
• Visa/relocation assistance
• Remote flexibility
• Stipends
• Bonus/commission
• Paid holidays