Data Engineer – AI, Analytics

Power Digital Marketing

Data Engineer building AI-ready pipelines, models, and semantic layers for Power Digital’s marketing clients. Using dbt, Python, SQL, Snowflake, and AI-agentic workflows.

Posted 8/23/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced proficiency in Python and SQL for building production-grade data pipelines and modeling, with a strong command of dbt and Snowflake. Capable of designing AI-ready data foundations and optimizing data workflows in a multi-tenant environment.

Highest-signal resume keywords

Python ProgrammingSQL ProficiencyDbt ExpertiseSnowflake KnowledgeAI-Agentic Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ModelingData IngestionData Quality MonitoringEnd-to-End Data Lifecycle ManagementIncremental StrategiesCloud Data StackMulti-Tenant Environment ExperienceAutomated TestingSemantic Layer Design

Soft Skills

CollaborationCommunicationIterative Development

Tools & Technologies

DbtSnowflakeGCPCursorClaude CodeGitHub CopilotGitCI/CD

Industry Keywords

Marketing DatasetsAttribution WindowsAPI ResilienceClient-Specific MartsAI Features

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

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 and reconcile spend, impressions, conversions, and revenue across Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft
Create customer-level joins across Shopify, Klaviyo, GA4, and client CRMs
Contribute to client-bespoke modeling, custom logic, overrides, and client-specific marts
Build semantic layers and metric definitions for consistent AI-generated SQL answers
Use AI-agentic workflows and AI coding tools to accelerate development and build intelligent data infrastructure
Document effective AI-agentic development patterns for team adoption
Collaborate with nova product and engineering, AI/innovation, and client teams
Monitor and resolve data quality issues
Optimize pipelines for cost and performance across a multi-client warehouse
Deliver production-ready datasets and pipelines supporting AI, product, and client teams
Build unified, AI-ready data foundations and enable AI features

Requirements

What you’ll need

Proficiency in spoken and written English at an advanced level is required
3+ years in data or analytics engineering
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, generic and singular 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 clean semantic layers
Experience with Git and CI/CD best practices, including automated testing
Comfortable shipping iteratively and refining data products based on live feedback

Benefits

Comp & perks

Equal Opportunity Employer
Power Digital does NOT charge any application, processing, or training fee at any stage of the recruitment or hiring process