Data Engineer, AI & Analytics

powerdigitalmarketing; United States; Remote; USD 110,000 - 180,000; Full time

Job summary

Power Digital Marketing seeks an experienced data/analytics engineer to own the core data foundation for an AI-native growth agency. You will build pipelines, models, and data marts serving internal teams, clients, and AI initiatives across a large multi-tenant warehouse.

Responsibilities include designing scalable ingestion, modeling spend and conversions, delivering client-focused modeling, and creating semantic layers for AI-generated SQL.

Qualifications

• 3+ years in data or analytics engineering with production code experience.
• Deep dbt expertise including incremental models, tests, snapshots, and exposures.
• Strong Python and SQL for data pipelines and modeling.
• Solid Snowflake and cloud data stack experience, preferably GCP.
• Experience modeling in a multi-tenant environment and integrating marketing datasets.

Responsibilities

• Own the core data foundation from raw data to serving layers.
• Build ingestion resilient to API changes and data retroactivity.
• Model across sources to reconcile spend, conversions, revenue across platforms.
• Deliver client-specific modeling atop shared patterns.
• Create semantic layers and metrics for AI-generated SQL consistency.
• Incorporate AI-agentic workflows to accelerate development and standardize patterns.
• Monitor data quality and optimize pipelines for cost and performance.

Skills

Python

SQL

dbt

Snowflake

Data pipelines

GCP

Tools

GitHub Copilot

CI/CD

Job description

Role overview

Own the core data foundation for an AI-native growth agency, building pipelines, models, and data marts that serve internal teams, clients, and AI initiatives. The work spans raw platform ingestion through a semantic layer across a large multi-tenant warehouse, with AI-agentic workflows embedded into how you build. Marketing data is fragmented by design, so the role centers on reconciling spend, conversions, and revenue across many platforms.

Responsibilities

• Design, build, and maintain the core data foundation, owning the workflow from raw platform data through serving layers.
• Build ingestion that tolerates API changes, deprecated fields, rate limits, and retroactive restatement of conversion data without corrupting downstream models.
• Model across sources to reconcile spend, impressions, conversions, and revenue across major ad platforms, plus customer-level joins across e-commerce, email, analytics, and CRM systems.
• Deliver client-bespoke modeling on top of the core layer, extending shared patterns rather than forking them.
• Build semantic layers and metric definitions that let AI-generated SQL return consistent, correct answers.
• Use AI-agentic workflows, including AI coding tools, to accelerate development and document patterns that become team standards.
• Monitor and resolve data quality issues, optimizing pipelines for cost and performance across a multi-client warehouse.

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 dbt expertise: incremental strategies, Jinja and macros, packages, tests, snapshots, source freshness, exposures, and DAG hygiene.
• Strong command of Snowflake and the surrounding cloud data stack to operate autonomously.
• Experience modeling in a multi-tenant environment, with judgment about what belongs in a client layer versus the core.
• Working knowledge of marketing and advertising datasets, including UTMs, attribution windows, and the gap between platform-reported and warehouse-reported conversions.
• Familiarity with cloud-native infrastructure (GCP) and infrastructure-as-code principles.
• Practical adoption of AI-agentic development workflows such as Cursor, Claude Code, or GitHub Copilot.

Nice to have

• Experience serving both BI and AI consumers from the same data foundation.

Benefits and work setup

• Pacific time zone working hours expected.
• Advanced spoken and written English required for daily collaboration.