Data Engineer, AI & Analytics

Role overview

Build and operate the data foundation that supports analytics, client reporting, internal products, and AI-enabled applications. This end-to-end data engineering role covers ingestion, transformation, modeling, semantic layers, data marts, reliability, and cross-functional delivery in a multi-tenant environment. A major challenge is reconciling fragmented marketing data across platforms with different schemas, attribution rules, currencies, time zones, and retroactive updates.

Responsibilities

- Design, build, and maintain production pipelines from raw source data through modeled and serving layers used by analytics, product, client, and AI consumers. - Develop resilient ingestion processes that handle API changes, deprecated fields, rate limits, and revised conversion data without damaging downstream models. - Reconcile advertising and commerce data across major media, ecommerce, analytics, email, and CRM sources, including customer-level joins and shared metric definitions. - Build client-specific models, overrides, and marts while deciding when reusable logic belongs in the shared core rather than a client layer. - Create semantic layers and metric definitions that support consistent, trustworthy answers from AI-generated SQL and other analytical workflows. - Monitor data quality, improve cost and performance, use automated tests and reconciliations, and collaborate with product, engineering, analytics, tracking, operations, and client teams.

Requirements

- At least three years in data or analytics engineering, including at least one year owning a meaningful production dbt project. - Advanced Python and SQL skills focused on production-grade pipelines and modeling. - Deep dbt expertise, including incremental models, refresh strategies, Jinja, macros, packages, tests, snapshots, freshness checks, exposures, DAG organization, and materializations. - Strong Snowflake and cloud data-platform knowledge, with the ability to own foundational data systems independently. - Experience with multi-tenant data modeling and end-to-end data lifecycles from ingestion to serving. - Familiarity with marketing datasets, attribution windows, UTM structures, cloud infrastructure, infrastructure-as-code, and AI-assisted development workflows.

Benefits and work setup

The source requires advanced spoken and written English. Success is measured through reliable production execution, accurate and adopted data products, timely completion of client-specific modeling work, and delivery of major assets that support downstream teams or applications.