Data Engineer, AI & Analytics

Power Digital Marketing

Data Engineer building unified, AI-ready pipelines and semantic layers for Power Digital, a tech-enabled growth marketing firm. Supporting agency, client, product, and AI initiatives.

Posted 8/23/2026full-timeRemote • 🇦🇷 ArgentinaMid-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 AI-ready data models. Capable of managing end-to-end data lifecycles and optimizing multi-client data pipelines for performance and cost.

Highest-signal resume keywords

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingData IngestionData Quality MonitoringEnd-To-End Data Lifecycle ManagementAI-Ready Data ModelsIncremental StrategiesAutomated TestingMulti-Tenant Environment ExperienceSemantic Layer DevelopmentFeature Store Architecture

Soft Skills

CollaborationIterative ShippingProblem-Solving

Tools & Technologies

DbtSnowflakeGCPCursorClaude CodeGitHub CopilotGitCI/CD Best Practices

Industry Keywords

Marketing DatasetsAdvertising DatasetsAttribution WindowsUTMsPlatform ConversionsData MartsAI-Agentic Workflows

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 retroactively restated conversion data
Model and reconcile 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-specific modeling, custom logic, overrides, and bespoke data marts
Build semantic layers and metric definitions for consistent AI-generated SQL results
Use AI-agentic workflows and AI coding tools to accelerate development and build intelligent data infrastructure
Collaborate with nova product and engineering, AI/innovation, client service, BI, Tagging & Tracking, and Data Ops teams
Monitor and resolve data quality issues
Optimize multi-client warehouse pipelines for cost and performance
Deliver production-ready datasets and pipelines supporting agency, client, product, and AI consumers
Reduce fragmentation by building unified, AI-ready data foundations

Requirements

What you’ll need

Advanced spoken and written English proficiency 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
Deep expertise in dbt, including incremental strategies, 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 versus warehouse conversions
Experience designing and managing end-to-end data lifecycles from ingestion to serving
Familiarity with cloud-native infrastructure, specifically GCP, and infrastructure-as-code principles
Real adoption of AI-agentic development workflows using Cursor, Claude Code, or GitHub Copilot
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
Diversity and inclusion are emphasized as core to the company culture