Data Engineer, AI & Analytics

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

📋 Description

• Develop, construct, and sustain the foundational data infrastructure, which includes data ingestion, modeling, and data marts.

• Create robust ingestion processes that can adapt to ad-platform API modifications, deprecated fields, rate limitations, and retroactive conversion adjustments.

• Model and reconcile expenditure, impressions, conversions, and revenue across various platforms including Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, Shopify, Klaviyo, GA4, and client CRMs.

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

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

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

• Document effective patterns for AI-agentic development.

• Collaborate with product and engineering teams, AI/innovation teams, and client representatives.

• Monitor and address data quality concerns.

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

• Achieve KPIs related to AI-accelerated development, data quality, reliability of pipelines, client request throughput, and cross-functional enablement.

• Construct comprehensive data systems along with production-ready datasets and pipelines that facilitate AI features and support AI, product, agency, and client teams.

⛳️ Requirements

• Advanced proficiency in both spoken and written English.

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

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

• Advanced skills in Python and SQL.

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

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

• Experience with data modeling in a multi-tenant setting.

• Working knowledge of marketing and advertising datasets, including UTMs and attribution windows.

• Demonstrated experience in designing and overseeing complete data lifecycles from ingestion to serving.

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

• Real-world application of AI-agentic development workflows, using tools such as Cursor, Claude Code, or GitHub Copilot.

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

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

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

• Experience in agency, consultancy, or services roles, along with measurement work, server-side tagging, and retail/marketplace data is beneficial but not essential.

🏝️ Benefits

• Equal Opportunity Employer.

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