Description

Key Responsibilities:

• Design and deliver end-to-end data engineering pipelines, including batch and real-time streaming solutions.
• Lead implementation of:
• Cloud-based data lakehouse platforms integrating diverse data sources.
• Real-time data processing pipelines for operational and analytical use cases.
• Develop scalable ETL/ELT pipelines using PySpark, Scala, and SQL.
• Implement advanced data modeling solutions including 3NF, dimensional modeling, and enterprise data warehousing strategies.
• Design and build incremental data loading frameworks and metadata-driven ingestion pipelines.
• Establish data quality frameworks and governance standards.
• Implement and manage Unity Catalog, including fine-grained security and access controls.
• Leverage Databricks components such as:
• Delta Live Tables
• Autoloader
• Structured Streaming
• Databricks Workflows
• Integration with orchestration tools (e.g., Apache Airflow)
• Drive CI/CD automation, deployment strategies, and DevOps best practices.
• Optimize performance of pipelines, Spark jobs, and compute resources.
• Provide architectural guidance and technical leadership across cross-functional teams.
• Engage with stakeholders and clients to translate business requirements into scalable technical solutions.

Deep Expertise In:

• Databricks and cloud-native storage/compute platforms
• Apache Spark (batch & streaming)
• Delta Lake & Lakehouse architecture
• Distributed data processing systems 
• Strong hands-on programming skills in Python, PySpark, Scala, and SQL

Key Skills

Education

Bachelor's degree