About the role
We're looking for an experienced Data Engineer who can design and run the pipelines that turn messy, multi-source, first-party data into clean, reliable, analysis- and model-ready datasets. Complex transformation is the core of this role; you'll be the person who makes sure the data powering our analytics, personalization, and AI is trustworthy and fast.
Responsibilities:
- Build and maintain batch and streaming pipelines that ingest customer and behavioral data across many sources and channels (web, app, email, SMS, WhatsApp, CRM, ad platforms).
- Design and own complex transformations for cleaning, normalizing, deduplicating, joining, and modeling data across sources so it stays consistent and dependable.
- Build and maintain the data models and warehouse/lakehouse layers that power analytics, personalization, and ML.
- Own data quality, lineage, and observability: tests, monitoring, alerting, and clear documentation.
- Optimize pipelines for performance and cost across the cloud stack.
- Partner with product, analytics, and AI/ML teams so the data they need is available, well-shaped, and reliable.
Requirements:
- 6+ years in data engineering, with a clear track record of complex data transformation at scale.
- Strong SQL and Python. Solid experience with ETL/ELT design, data modeling, and orchestration (e. g., Airflow, dbt).
- Experience with a modern data warehouse or lakehouse (e. g., Redshift, BigQuery, Snowflake).
- Cloud experience: AWS and GCP are preferred. Comfort with data-quality frameworks, version control (Git), and CI/CD.
Nice to Have:
- Product exposure experience working on a data product or closely alongside product teams (a strong plus).
- Streaming / real-time data experience (Kafka, Kinesis) given our real-time personalization needs.
- Marketing, eCommerce, or customer/behavioral data experience.
- Familiarity with our stack: FastAPI, Python, SQL, AWS, GitHub, Jenkins.