Description

You will build and maintain data pipelines while migrating workloads from on-premise systems to GCP.

This role is on-site.

Responsibilities

• Develop Hive queries using joins and partitions to process large datasets and load filtered data to edge node tables.
• Build Spark jobs using Scala and Python (PySpark) APIs and use Spark SQL to create structured data.
• Write shell scripts to schedule full and incremental loads and automate metadata synchronization between GCS and GCP Hive.
• Migrate data from GCP Google Cloud Storage Hive to BigQuery and flatten on-prem data for ingestion into Druid.
• Troubleshoot performance issues, tune Spark application parallelism, and resolve production connectivity or data quality issues.

Required Skills

• 5+ years of experience in data engineering.
• Proficiency in HiveQL for data analysis and large-scale data processing.
• Hands-on experience developing Spark jobs using Scala and Python (PySpark).
• Experience with Spark SQL and performance tuning for memory and parallelism.
• Practical knowledge of BigQuery and Google Cloud Storage (GCS).
• Ability to develop shell scripts for job scheduling and automation.
• Experience managing data ingestion from on-premise sources to Druid.
• Experience with Hive optimization techniques and data validation.

Preferred Skills

• Master’s degree in Computer Science, Computer Engineering, Data Analytics, or an MBA.

Key Skills

Education

Any Graduate