Responsibilities
- Pipeline Engineering: Design, build, and maintain scalable ETL/ELT pipelines from diverse sources including Data Lakes, Production ClickHouse instances, flat files, and various APIs.
- Infrastructure & Orchestration: Configure and optimize our Data Warehouse infrastructure (ClickHouse) and orchestration layers (Mage.ai).
- Engineering Excellence: Implement and manage "engineering-grade" CI/CD workflows, conduct rigorous PR reviews, and ensure robust dependency management across the stack.
- Data Modeling & Architecture: Implement Medallion architecture (Bronze/Silver/Gold) and maintain high-performance data models using dbt.
- Quality & Observability: Build automated data quality monitoring and alerting; proactively escalate upstream data issues to engineering teams and keep stakeholders informed of pipeline health.
- Advanced Data Flows: Develop reverse ETL (rETL) pipelines and expose secure data APIs to enable seamless data consumption across the organization.
- Strategic Integration: Manage event streaming and real-time data ingestion (Kafka, CDC) to support high-volume product analytics and tracking.
4 7 years of experience in Data Engineering, preferably within the e-commerce or high-growth tech industry.
- Expert-level Python and SQL (able to write highly optimized code for large-scale datasets).
- Deep experience with dbt for transformation and modeling.
- Strong experience with ClickHouse (preferred) or similar modern warehouses (Snowflake, BigQuery) and Orchestration tools: mage.ai (preferred) or similar tools (Airflow).
- Proven experience implementing and managing Reverse ETL workflows to sync data back into operational tools.
- Proven track record building and deploying production-grade CI/CD pipelines and automation scripts.
- Solid understanding of Data Contracts, Medallion architecture, and Data Quality frameworks.
