Data Engineer, AI & Analytics

Power Digital Marketing

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

Posted 8/23/2026full-timeRemote • 🇵🇪 PeruMid-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 end-to-end data systems. Capable of architecting AI-ready data models and optimizing data pipelines for performance and cost across multi-client environments.

Highest-signal resume keywords

Advanced Proficiency In PythonAdvanced Proficiency In SQLDeep Expertise In DbtStrong Command Of SnowflakeExperience With Cloud-Native Infrastructure (GCP)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ModelingIngestion StrategiesAI-Agentic Development WorkflowsEnd-To-End Data Lifecycle ManagementIncremental StrategiesAutomated TestingFeature StoresSemantic LayersMulti-Tenant Environment

Soft Skills

CollaborationCommunicationProblem-Solving

Tools & Technologies

GitCI/CDCursorClaude CodeGitHub Copilot

Industry Keywords

Marketing DatasetsAdvertising DatasetsAttribution WindowsServer-Side TaggingRetail/Marketplace Data

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 resilient ingestion for ad-platform API changes, deprecated fields, rate limits, and retroactive conversion restatements
Model and reconcile spend, impressions, conversions, and revenue across Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, Shopify, Klaviyo, GA4, and client CRMs
Contribute to client-bespoke modeling, custom logic, overrides, and client-specific data 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
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
Meet KPIs for AI-accelerated development, data quality, pipeline reliability, client request throughput, and cross-functional enablement
Build end-to-end data systems and production-ready datasets and pipelines that enable AI features and support AI, product, agency, and client teams

Requirements

What you’ll need

Advanced proficiency in spoken and written English
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, Jinja, macros, packages, tests, snapshots, source freshness, exposures, DAG, 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 and attribution windows
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
Ability to architect AI-ready data models, including feature stores and semantic layers
Experience with Git and CI/CD best practices and automated testing
Comfortable shipping iteratively and refining data products based on live feedback
Agency, consultancy, or services experience, measurement work, server-side tagging, and retail/marketplace data are helpful but not required

Benefits

Comp & perks

Equal Opportunity Employer
People-first culture valuing diversity in backgrounds and experiences