About The Position

Build and maintain data pipelines that process high -volume subscriber data. Work with upstream systems to collect raw data and prepare it for downstream consumption. Design table structures and develop ETL workflows in Databricks and Snowflake. Develop automated Data Quality checks and enforce data reliability standards. Use Airflow for orchestration and schedule management. Tune SQL and Spark jobs for performance at large scale. Deploy schema changes using Schema Change or similar tools. Partner with analytics, infrastructure, and product teams in a fast -paced environment. Support both net -new development and enhancements to existing pipelines.

Requirements

  • 3+ years of hands -on data engineering experience.
  • Strong SQL expertise with proven performance tuning skills.
  • Proficient in PySpark, Spark, and Python for large -scale data processing.
  • Experience with Databricks and Snowflake.
  • Strong understanding of data modeling and ETL best practices.
  • Experience with data orchestration tools such as Airflow.
  • Ability to work with large data volumes (tens to hundreds of millions of records per day).
  • Strong communication and analytical skills.
  • Comfortable working in a highly collaborative Agile environment.
  • Bachelor’s degree required.

Responsibilities

  • Build and maintain data pipelines that process high -volume subscriber data.
  • Work with upstream systems to collect raw data and prepare it for downstream consumption.
  • Design table structures and develop ETL workflows in Databricks and Snowflake.
  • Develop automated Data Quality checks and enforce data reliability standards.
  • Use Airflow for orchestration and schedule management.
  • Tune SQL and Spark jobs for performance at large scale.
  • Deploy schema changes using Schema Change or similar tools.
  • Partner with analytics, infrastructure, and product teams in a fast -paced environment.
  • Support both net -new development and enhancements to existing pipelines.
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