Data Engineer, AI & Analytics

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

📋 Description

• Design, construct, and uphold the core data framework, which encompasses data ingestion, modeling, and data marts.

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

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

• Contribute to custom modeling for clients, incorporating unique logic, overrides, and tailored data marts.

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

• Leverage AI-agentic workflows and coding tools to expedite development and establish intelligent data infrastructure.

• Document effective AI-agentic development practices for team-wide implementation.

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

• Monitor and address any data quality challenges.

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

• Deliver production-ready datasets and pipelines that support AI initiatives, product development, agency functions, and client teams.

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

⛳️ Requirements

• 3+ years of experience in data or analytics engineering.

• At least 1 year of ownership of a dbt project of significant size in a production environment.

• Advanced skills in Python and SQL.

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

• Strong expertise in Snowflake and the associated cloud data stack.

• Experience with modeling in a multi-tenant environment.

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

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

• Knowledge of cloud-native infrastructure, particularly Google Cloud Platform (GCP), and principles of infrastructure-as-code.

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

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

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

• Proficient in spoken and written English is required.

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

🏝️ Benefits

• Equal Opportunity Employer.

• Commitment to diversity and inclusion, valuing individuals across race, gender identity, age, disability status, veteran status, sexual orientation, religion, and other identities.