Data modeling & transformation
Build and maintain DBT models in Snowflake — design layered architecture (staging, intermediate, marts), write data quality tests (Elementary)
Write clean, well-structured Python and SQL; participate in code reviews and help define engineering standards for the data team
Design and maintain data pipelines using Airflow (DAGs in Python)
Manage data integrations via Airbyte — starting with marketing sources (GA4, Facebook Ads, TikTok Ads) and expanding to other streams over time
Use AWS S3 as the primary storage layer — manage buckets, partitioning, and access control
Own CI/CD pipelines for data workflows — automated testing, deployment, and monitoring
Monitor data quality and pipeline reliability; proactively identify and fix issues before they reach downstream consumers
Work closely with analysts to understand their data needs and translate them into well-modeled datasets
Bridge the gap between raw data and analyst-ready tables — create models clearly so analysts can work independently

4+ years building production data pipelines
Strong hands-on experience with Snowflake and DBT (models, tests)
Advanced SQL — you write it fluently and can optimize it when needed
Python proficiency for Airflow DAG development and ETL development
Basic AWS knowledge (S3, IAM — enough to operate independently)
Basic understanding of CI/CD
Nice to have: Experience with Power BI, OpenMetadata
Familiarity with marketing data sources: GA4, Facebook Ads API, TikTok Ads API
Knowledge of marketing attribution models (first-touch, last-touch, data-driven)

Data Engineer

Key skills