Description

You will build and maintain large-scale data processing and storage systems within a complex Big Data environment.

This role is on-site.

Responsibilities

• Develop data ingestion, validation, transformation, and engineering code alongside the data engineering team.
• Build open-source platform components using Databricks, Hadoop, Spark, Scala, Java, Oozie, and Hive.
• Deliver solutions on cloud platforms and integrate with services like Azure Data Factory, ADLS, Azure DevOps, AWS Glue, Redshift, Lambda, or S3.
• Document code artifacts, user documentation, and run books.
• Troubleshoot deployments across environments, provide testing support, and participate in design sessions, demos, prototypes, and training workshops.

Required Skills

• 5+ years of experience working in complex Big Data and multi-vendor environments.
• 5+ years of experience with JIRA, GitHub, Git, and other code management tools.
• 3+ years of experience with large Hadoop projects using Spark and Python, including Spark DataFrame, Dataset APIs, SparkSQL, RDDs, and Scala function literals.
• 2+ years of experience developing large-scale data processing, storage, or distribution systems, preferably with Databricks.
• Hands-on experience with Hadoop, Hive, Sqoop, Oozie, and HDFS.
• Strong SQL skills and experience with Postgres or MySQL RDBMS platforms.
• Experience with ELT/ETL development, patterns, and tooling.
• Experience with AWS and/or Azure cloud environments.
• Experience with Linux (RHEL or CentOS), IDEs, unit testing frameworks, and Maven.

Preferred Skills

• Bachelor's degree in Computer Science or a related field.
• Certifications in Spark, Databricks, AWS, Azure, or other cloud platforms.

Key Skills

Education

Any Graduate