Data Engineer, AI & Analytics

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

📋 Description

• Create, develop, and sustain the foundational data architecture, encompassing data ingestion, modeling, and data marts.

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

• Model data across platforms such as Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, Shopify, Klaviyo, GA4, and various client CRMs.

• Assist in client-specific modeling and the creation of custom data marts while enhancing the shared data foundation.

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

• Leverage AI workflows and coding tools to expedite development and create a sophisticated data infrastructure.

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

• Oversee and rectify data quality challenges while optimizing pipelines for cost-effectiveness and performance across a multi-client data warehouse.

• Design and deploy comprehensive data systems and production-ready datasets and pipelines that support AI, product development, agency work, and client initiatives.

• Mitigate data fragmentation by constructing unified, AI-ready data foundations.

⛳️ Requirements

• Advanced proficiency in both spoken and written English is essential for this position.

• A minimum of 3 years in data or analytics engineering, including over 1 year managing a dbt project of substantial scope in a production setting.

• Expertise in Python and SQL, with an emphasis on creating production-grade code for data pipelines and modeling.

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

• Strong understanding of Snowflake and the associated cloud data stack.

• Experience in modeling within a multi-tenant framework.

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

• Proven track record in designing and managing end-to-end data lifecycles from ingestion through to serving.

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

• Actual 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 semantic layers.

• Proficiency 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

• Competitive salary and performance-based incentives.

• Flexible work arrangements to support work-life balance.

• Opportunities for professional development and continuous learning.

• Access to cutting-edge tools and technologies.

• Collaborative and inclusive work environment.