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