Meaningful work,
meaningful life

Key Responsibilities

• Analyze existing Hadoop, Pig, and Spark scripts from Dataproc and refactor them into Databricks-native PySpark.

• Implement data ingestion and transformation pipelines using Delta Lake best practices.

• Apply conversion rules and templates for automated code migration and testing.

• Conduct data validation between legacy and migrated environments (schema, count, and data-level checks).

• Collaborate on developing AI-driven tools for code conversion, dependency extraction, and error remediation.

• Ensure best practices for code versioning, error handling, and performance optimization.

• Participate in UAT, troubleshooting, and post-migration validation activities.

Technical Skills

• Core: Python, PySpark, SQL

• Databricks: Delta Lake, Unity Catalog, Databricks Workflows, MLflow (basic understanding)

• GCP: Dataproc, BigQuery, GCS, Composer/Airflow, Cloud Functions

• Data Engineering: Hadoop, Hive, Pig, Spark SQL

• Automation: Experience with migration utilities or AI-assisted code transformation tools

• CI/CD: Git, Jenkins, Terraform (preferred)

• Validation: Data comparison utilities (Delta-to-Delta, DataFrame diffing, schema validation)

Preferred Experience

• 5–8 years in data engineering or big data application development.

• Hands-on experience migrating Spark or Hadoop workloads to Databricks.

• Familiarity with Delta architecture, data quality frameworks, and GCP cloud integration.

• Exposure to GenAI-based tools for automation or code refactoring is a plus.