Data Engineer, AI & Analytics

This is a fully remote position, open to applicants in Ecuador.

📋 Description

• Design, construct, and sustain the fundamental data infrastructure, encompassing data ingestion, modeling, and data marts.

• Develop ingestion processes that are resilient to changes in APIs, deprecated fields, rate limits, and retroactive conversion adjustments.

• Model expenditures, impressions, conversions, and revenue across platforms such as Meta, Google, TikTok, Amazon, LinkedIn, and Microsoft.

• Establish customer-level joins across systems like Shopify, Klaviyo, GA4, and client CRMs.

• Contribute to custom modeling tailored to clients, including bespoke logic, overrides, and client-specific data marts.

• Create semantic layers and metric definitions to ensure consistent AI-generated SQL responses.

• Leverage AI-driven workflows and coding tools to enhance development speed and establish intelligent data infrastructure.

• Document effective AI-driven development patterns for team-wide adoption.

• Collaborate with Nova product and engineering teams, AI/innovation, Client Service, BI, Tagging & Tracking, Data Ops, and client teams.

• Monitor and rectify data quality issues as they arise.

• Optimize data pipelines for cost-effectiveness and performance across a multi-client warehouse.

• Deliver comprehensive data systems and production-ready datasets that empower AI functionalities and support AI, product, agency, and client teams.

• Achieve established KPIs related to AI-accelerated development, data quality, pipeline reliability, client request processing, and cross-functional support.

⛳️ Requirements

• A minimum of 3 years of experience in data or analytics engineering.

• At least 1 year of experience managing a dbt project of significant scale in a production environment.

• Advanced skills in Python and SQL.

• Extensive knowledge of dbt, including incremental strategies, full-refresh considerations, Jinja, macros, packages, tests, snapshots, source freshness, exposures, DAG management, and materializations.

• Strong proficiency with Snowflake and the broader cloud data ecosystem.

• Experience in modeling within a multi-tenant environment.

• Familiarity with marketing and advertising datasets, such as UTMs, attribution windows, and the differences between platform-reported and warehouse-reported conversions.

• Proven track record of designing and managing complete data lifecycles from ingestion to serving.

• Understanding of cloud-native infrastructure (GCP) and principles of infrastructure-as-code.

• Practical experience with AI-driven development workflows, including tools like Cursor, Claude Code, or GitHub Copilot.

• Demonstrated capability to architect AI-ready data models, including feature stores and clean semantic layers.

• Experience with Git and CI/CD best practices, including automated testing methodologies.

• Comfortable with iterative shipping and refining of data products based on real-time feedback.

• Proficiency in spoken and written English at an advanced level is essential.

🏝️ Benefits

• Equal Opportunity Employer.

• A people-first culture that values diversity in backgrounds and experiences.

• No application, processing, or training fees at any stage of the recruitment or hiring process.