Data Engineer, AI & Analytics

Power Digital Marketing

. Design, build, and maintain the core data foundation, including ingestion, modeling, and data marts .

Posted 9/29/2026full-timeRemote • ColombiaMid-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 models and managing end-to-end data lifecycles in a multi-tenant environment.

Highest-signal resume keywords

Python ProgrammingSQL ProficiencyDbt ExpertiseSnowflake KnowledgeAI-Ready Data Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ModelingData Pipeline DevelopmentIncremental StrategiesFull-Refresh TradeoffsJinjaMacrosAutomated TestingMulti-Tenant Environment ExperienceEnd-to-End Data Lifecycle Management

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

DbtSnowflakeGCPCursorClaude CodeGitHub CopilotGitCI/CD

Industry Keywords

Data FoundationData QualityAI-Agentic WorkflowsMarketing DatasetsAttribution Windows

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 data across Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, Shopify, Klaviyo, GA4, and client CRMs
Contribute to client-bespoke modeling and client-specific data marts while extending the shared foundation
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, Data Ops, and client teams
Monitor and resolve data quality issues and optimize pipelines for cost and performance across a multi-client warehouse
Build and ship end-to-end data systems and production-ready datasets and pipelines supporting AI, product, agency, and client teams
Reduce data fragmentation by building unified, AI-ready data foundations

Requirements

What you’ll need

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