Databricks Data Engineer

Essential Technical Skills

• Data Engineering: Strong foundation in data engineering principles, ETL/ELT processes, and data pipeline design patterns
• PySpark: Proven hands-on experience developing data pipelines using PySpark, including DataFrames API, Spark SQL, and performance optimization
• Databricks Platform: Practical experience with Databricks workspace, cluster management, notebooks, and job orchestration
• Workspace AI Agent: Knowledge of Databricks Workspace AI Agent capabilities and integration
• Data Modelling: Experience implementing data models including dimensional modeling, data vault, or lakehouse architectures
• Delta Lake: Understanding of Delta Lake features including ACID transactions, schema evolution, and optimization techniques
• Python: Strong Python programming skills for data processing and automation

Additional Technical Skills

• SQL proficiency for data querying and transformation
• Experience with cloud platforms (Azure, AWS, or GCP)
• Understanding of data governance and security best practices
• Knowledge of streaming data processing (Structured Streaming)
• Familiarity with DevOps practices and CI/CD pipelines
• Experience with version control systems (Git)
• Understanding of data quality frameworks and testing methodologies

Professional Experience

• Minimum 8 years in data engineering or related roles
• At least 2-3 years of hands-on experience with Databricks platform
• Proven track record of refactoring legacy code to modern frameworks
• Experience building and maintaining production data pipelines at scale
• Background working across multiple data sources and formats
• Experience in agile development environments

Required Certifications

• Databricks Certified Data Engineer Associate OR Databricks Certified Data Engineer Professional

Data Pipeline Development & Operations

• Design, build, and operate scalable and reliable data pipelines on the Databricks platform
• Develop end-to-end data workflows from ingestion through transformation to consumption
• Implement robust error handling, monitoring, and alerting mechanisms
• Ensure data pipeline reliability, performance, and maintainability
• Optimize pipeline performance through efficient Spark job design and cluster configuration
• Manage and orchestrate complex data workflows using Databricks Jobs and workflows

Legacy Code Modernization

• Refactor legacy code and data pipelines to PySpark for improved performance and scalability
• Migrate traditional ETL processes to modern ELT patterns on Databricks
• Assess existing codebases and identify opportunities for optimization and modernization
• Ensure backward compatibility and data integrity during migration processes
• Document refactoring approaches and create migration playbooks
• Collaborate with stakeholders to minimize disruption during code transitions

RESPONSIBILITIES:

Data Engineering Excellence

• Implement data quality checks and validation frameworks
• Design and maintain Delta Lake tables with appropriate optimization strategies
• Develop reusable code libraries and frameworks for common data engineering tasks
• Follow software engineering best practices including version control, testing, and CI/CD
• Participate in code reviews and provide constructive feedback to team members
• Troubleshoot and resolve data pipeline issues in production environments

Collaboration & Knowledge Sharing

• Work closely with data architects, analysts, and business stakeholders
• Collaborate with Infrastructure (Infra), Applications (Apps), and Cyber teams
• Share knowledge and best practices with Team NCS
• Mentor junior data engineers on PySpark and Databricks technologies
• Document technical solutions and maintain comprehensive documentation