Data Engineer, AI & Analytics

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

📋 Description

• Design, construct, and uphold the primary data infrastructure, which encompasses data ingestion, modeling, and data marts.

• Develop robust ingestion processes to accommodate changes in ad-platform APIs, deprecated fields, rate limits, and retroactively amended conversion data.

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

• Create customer-level joins integrating Shopify, Klaviyo, GA4, and client CRMs.

• Contribute to tailored modeling for clients, including custom logic, overrides, and specialized data marts.

• Establish semantic layers and metric definitions to ensure consistency in AI-generated SQL outcomes.

• Leverage AI-driven workflows and coding tools to expedite development and construct an intelligent data infrastructure.

• Collaborate with teams from nova product and engineering, AI/innovation, client service, BI, Tagging & Tracking, and Data Ops.

• Oversee and resolve issues related to data quality.

• Optimize warehouse pipelines for multiple clients to enhance cost-effectiveness and performance.

• Provide production-ready datasets and pipelines that support agency, client, product, and AI users.

• Minimize fragmentation by developing unified, AI-compatible data foundations.

⛳️ Requirements

• Advanced proficiency in spoken and written English is mandatory.

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

• At least 1 year of experience managing a dbt project of substantial size in a production setting.

• High-level proficiency in Python and SQL.

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

• Strong understanding of Snowflake and its associated cloud data ecosystem.

• Experience in modeling within a multi-tenant environment.

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

• Proven experience in designing and managing comprehensive data lifecycles from ingestion to serving.

• Knowledge of cloud-native infrastructure, particularly GCP, and principles of infrastructure-as-code.

• Genuine experience with AI-driven development workflows utilizing Cursor, Claude Code, or GitHub Copilot.

• Capability to design AI-ready data models, including feature stores and organized semantic layers.

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

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

🏝️ Benefits

• Equal Opportunity Employer.

• Emphasis on diversity and inclusion as fundamental aspects of the company culture.