Data Engineer, AI & Analytics

Power Digital Marketing

Data Engineer building AI-ready pipelines, models, and semantic layers for Power Digital’s growth marketing and data intelligence services. Supporting agency, client, product, and AI teams.

Posted 8/23/2026full-timeRemote • 🇪🇨 EcuadorMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced proficiency in Python and SQL, with deep expertise in dbt and experience in building resilient data ingestion pipelines. Capable of architecting AI-ready data models and optimizing data systems for performance and cost across multi-tenant environments.

Highest-signal resume keywords

Advanced Proficiency In PythonDeep Expertise In DbtStrong Command Of SnowflakeExperience With Git And CI/CDFamiliarity With Cloud-Native Infrastructure (GCP)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringSQLPythonDbtData ModelingData IngestionAI-Agentic DevelopmentData Quality MonitoringEnd-To-End Data Lifecycle ManagementIncremental Strategies

Soft Skills

CollaborationCommunicationIterative DevelopmentProblem-Solving

Tools & Technologies

SnowflakeGCPGitCI/CDCursorClaude CodeGitHub Copilot

Industry Keywords

Marketing DatasetsAttribution WindowsMulti-Tenant EnvironmentData Quality IssuesAI 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 spend, impressions, conversions, and revenue across Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft
Build 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, Client Service, BI, Tagging & Tracking, Data Ops, and client teams
Monitor and resolve data quality issues
Optimize pipelines for cost and performance across a multi-client warehouse
Deliver end-to-end data systems and production-ready datasets that enable AI features and support AI, product, agency, and client teams
Meet defined KPIs for AI-accelerated development, data quality, pipeline reliability, client request throughput, and cross-functional enablement

Requirements

What you’ll need

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, 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 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
Advanced spoken and written English proficiency is required

Benefits

Comp & perks

Equal Opportunity Employer
People-first culture valuing diversity in backgrounds and experiences
No application, processing, or training fee at any stage of recruitment or hiring