Data Engineer
18 hours ago
Role summary
Build robust, scalable data pipelines and platforms that deliver clean, reliable, and timely data for analytics and AI.
Key responsibilities
- Design ELT or ETL pipelines and orchestrate with Airflow or Prefect.
- Model data for analytics, implement CDC, and manage schemas.
- Add data quality tests, lineage, and cataloging.
- Tune warehouses for performance and cost.
- Secure data with role-based access and compliance controls.
Minimum qualifications
- 3+ years in data engineering with strong SQL and Python.
- Experience with BigQuery or Snowflake or Redshift and an orchestrator.
- Version control and CI for data.
- Basic understanding of dbt or similar transformation frameworks.
Preferred
- Streaming with Kafka or pub/sub.
- Terraform and cloud data services.
- Great Expectations or similar testing frameworks.
Tools and stack
Airflow or Prefect, dbt, Kafka or Pub/Sub, Python, SQL, Terraform, Great Expectations, BigQuery or Snowflake or Redshift, Looker or Power BI as downstream.
KPIs / success metrics
Pipeline SLA and reliability, data quality scores, cost per terabyte, time to data availability.
Initial screening questions
- How would you design CDC from a high-volume OLTP source into a warehouse?
- How do you validate models and prevent schema drift?
- Show how you balance cost and performance for a slowly changing dimension.
Optional skills task
Build an ELT pipeline with dbt models and tests on a sample dataset, then generate lineage docs.
Career path
Senior Data Engineer → Staff Data Engineer → Platform or Data Architecture Lead.
Job Type: Full-time
Work Location: On the road
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