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 initiatives.

Posted 8/23/2026full-timeRemote • 🇪🇸 SpainMid-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 workflows across multi-tenant environments.

Highest-signal resume keywords

Advanced Proficiency In PythonDeep Expertise In DbtStrong Command Of SnowflakeExperience With AI-Agentic Development Workflows3+ Years In Data Or Analytics Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonDbtData ModelingData IngestionAutomated TestingMulti-Tenant EnvironmentData Lifecycle ManagementAI-Ready Data ModelsIncremental Strategies

Soft Skills

Advanced English ProficiencyCollaboration

Tools & Technologies

SnowflakeGCPCursorClaude CodeGitHub CopilotGitCI/CD

Industry Keywords

Marketing DatasetsAdvertising DatasetsUTMsAttribution WindowsData QualityData PipelinesSemantic LayersFeature Stores

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 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 production-ready datasets and pipelines supporting AI, product, client, agency, BI, and internal consumers
Meet KPIs for development speed, data quality, pipeline reliability, client request throughput, and cross-functional enablement

Requirements

What you’ll need

Proficiency in spoken and written English at an advanced level
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 and attribution windows
Experience designing and managing end-to-end data lifecycles from ingestion to serving
Familiarity with cloud-native infrastructure, specifically GCP
Familiarity with infrastructure-as-code principles
Experience using AI-agentic development workflows and tools such as Cursor, Claude Code, and GitHub Copilot
Ability to architect AI-ready data models, including feature stores and semantic layers
Experience with Git and CI/CD best practices
Experience with automated testing
Comfortable shipping iteratively and refining data products based on live feedback

Benefits

Comp & perks

Equal Opportunity Employer
Diversity and inclusion-focused workplace
AI-agentic development workflows as part of the role
Opportunity to work on AI, product, client, and agency initiatives
Autonomy and professional growth through ownership of foundational data systems