Job Description

  • Build & Optimize Pipelines: Develop high-throughput ETL workflows using PySpark on Databricks.
  • Data Architecture & Engineering: Work on distributed computing solutions, optimize Spark jobs, and build efficient data models.
  • Performance & Cost Optimization: Fine-tune Spark configurations, optimize Databricks clusters, and reduce compute/storage costs.
  • Collaboration: Work closely with Data Scientists, Analysts, and DevOps teams to ensure data reliability.
  • ETL & Data Warehousing: Implement scalable ETL processes for structured & unstructured data.
  • Monitoring & Automation: Implement logging, monitoring, and alerting mechanisms for data pipeline health and fault tolerance.

Skills Required
Hive, Amazon Redshift, Pyspark, Data Warehousing

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