Data Engineer

one of the world's most impactful shopping recommendation companies, is searching for a talented Data Engineer to join our innovative team. For the past decade, we've served tens of millions of users globally, and we're looking for a passionate individual to help us continue our journey of making an impact.
What You'll Own:
Data Infrastructure
Build and operate data pipelines on GCP using Cloud Workflows, Cloud Functions, Cloud Scheduler, and BigQuery.
Create, Transform and Manage Datasets (Python, DBT,..)
Design and implement API integrations to pull data from affiliate networks, ad platforms, and internal services.
Ensure pipeline reliability, observability (monitoring, alerting, SLA management), and scalability.
Data Quality & Trust
Implement tests, freshness checks, and anomaly detection across all critical models.
Lead incident triage and root-cause analysis when data issues arise; build systems that catch problems before stakeholders do.
Maintain clear documentation of data contracts, schema definitions, and transformation logic.
ML & Data Science Collaboration
Work hand-in-hand with Data Scientists building ML models - ensu

דרישות:

3-5+ years of Data Engineering or Backend Engineering experience with clear delivery in production systems.
Bachelor's (or higher) in Computer Science, Engineering, Mathematics, or a quantitative field (or equivalent background).
Strong Python skills: writing production-grade ETL/pipeline code, not just scripts.
SQL proficiency: complex window functions, CTEs, query optimization, and BigQuery-flavored SQL.
dbt: modeling patterns, incremental strategies, testing, and documentation.
GCP: hands-on experience with BigQuery, Cloud Functions, Cloud Workflows, or Cloud Scheduler (or equivalent cloud stack with willingness to ramp up quickly).
API integrations: building robust data ingestion from REST APIs with retries, pagination, and error handling.
Data quality mindset: you think about freshness, schema drift, nulls, and SLA before you're asked.
Nice‑to‑Haves:
Experience with affiliate marketing data (Amazon Associates, CJ, Impact, etc.) or ad platform APIs (Meta, Google Ads).
Familiarity with ML pipelines: feature engineering, training data prep, or serving infrastructure.
Event-driven architectures: Pub/Sub, Cloud Run, or equivalent.