Data Engineer, AI & Analytics

Power Digital Marketing

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

Posted 8/23/2026full-timeRemote • 🇲🇽 MexicoMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced proficiency in Python and SQL, with deep expertise in dbt and the ability to architect AI-ready data models. Skilled in managing end-to-end data lifecycles and optimizing data pipelines for performance and cost across multi-tenant environments.

Highest-signal resume keywords

Advanced Proficiency In PythonAdvanced Proficiency In SQLDeep Expertise In DbtStrong Command Of SnowflakeExperience With Git And CI/CD Best Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ModelingAPI IngestionIncremental StrategiesFull-Refresh TradeoffsJinjaMacrosAutomated TestingFeature StoresSemantic Layers

Soft Skills

CollaborationIterative ShippingProblem Solving

Tools & Technologies

SnowflakeGCPCursorClaude CodeGitHub Copilot

Industry Keywords

Marketing DatasetsAttribution WindowsData QualityMulti-Tenant EnvironmentAI-Agentic Development

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 marketing data across Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, 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
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 production-ready datasets and pipelines supporting AI, product, agency, and client teams
Reduce data fragmentation through unified, AI-ready data foundations

Requirements

What you’ll need

3+ years in data or analytics engineering
1+ year 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, DAGs, 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
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, 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, including automated testing
Advanced spoken and written English proficiency required
Comfortable shipping iteratively and refining data products based on live feedback

Benefits

Comp & perks

Equal Opportunity Employer
Diversity and inclusion valued across race, gender identity, age, disability status, veteran status, sexual orientation, religion, and other identities