Data Engineer, AI & Analytics

About the role

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer, AI & Analytics based in United States. This role sits at the heart of a data organization responsible for the pipelines, models, and data products powering analytics, client delivery, and AI initiatives. You will own the data lifecycle end to end, from raw ingestion through semantic and serving layers, across a complex multi-client environment. The work involves solving challenging data engineering problems created by fragmented marketing platforms, inconsistent schemas, attribution logic, currencies, and time zones. You will build scalable foundations across advertising and commerce data while ensuring reliability, accuracy, and usability for both BI and AI applications. AI-agentic development will be a core part of the engineering workflow, helping accelerate coding, debugging, architecture, and delivery. You will collaborate closely with product, engineering, client, analytics, tracking, and data operations teams to turn evolving requirements into production-ready solutions. This is an opportunity for an experienced data engineer to shape AI-ready infrastructure while working on high-impact problems across a diverse portfolio of clients and technologies.

Responsibilities

• Design, build, and maintain the core data foundation, including ingestion pipelines, data models, semantic layers, and data marts supporting agency, client, internal, and AI consumers.
• Own the full workflow from raw platform data through production serving layers, ensuring reliable, scalable, and well-governed data products.
• Build resilient ingestion pipelines capable of handling API changes, deprecated fields, rate limits, retroactive conversion restatements, and other real-world platform behaviors without compromising downstream data quality.
• Develop unified models across marketing and advertising platforms such as Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft, as well as customer-level integrations with Shopify, Klaviyo, GA4, and client CRM systems.
• Develop and maintain client-specific models, overrides, and bespoke data marts while determining when new logic should remain client-specific versus becoming part of the shared data foundation.
• Build semantic layers and standardized metric definitions that enable reliable analytics and consistent AI-generated SQL results.
• Use AI-agentic development workflows and AI coding tools to accelerate engineering, debugging, architecture, and delivery while documenting successful patterns for broader team adoption.
• Collaborate with product and engineering teams, AI and innovation teams, client-facing teams, and other stakeholders to translate requirements into scalable data solutions.
• Monitor data quality, investigate issues, improve pipeline reliability, and optimize warehouse performance and infrastructure costs across a multi-client environment.
• Deliver production-ready datasets and pipelines that enable AI features, product capabilities, analytics, and client-facing solutions.
• Establish and maintain automated testing, reconciliation